rafmacalaba/datause-encoder-data huggingface dataset:
# **Testing the Importance of Search Frictions, Matching, and Reservation Prestige Through Randomized Experiments in Jordan**<sup>**#**</sup>

Matthew Groh, _World Bank_ David McKenzie, _World Bank, BREAD, CEPR and IZA_ Nour Shammout, _University of Michigan and World Bank_ Tara Vishwanath, _World Bank_ _Keywords_ : psychometrics, labor market matching, reservation prestige, youth unemployment, Jordan, Randomized Experiment.

_JEL classification codes_ : O12, O15, J64, J08.

# We thank the TFESSD trust fund, the Knowledge for Change (KCP) trust fund, and the Research Support Budget (RSB) of the World Bank for their generous support for this project; Dr Marwan Al-Zoubi for his collaboration on developing the psychometric measures and matching process; the Business Development Center (BDC) and Dajani Consulting for their work in project implementation and surveying, respectively; Galal Ali for excellent research assistance; and Nandini Krishnan for her collaboration on related aspects of this work.
laere and Mairesse (2013) predicted that competition among employers will result in a single market wage, whereby a small cut in wage by an employer will result in immediate resignation of all workers.

On the other hand, factors such as the lack of perfect information on alternative job opportunities, or search and moving costs can give firms significant market power over their workers. Such market conditions can readily give rise to the situation where a firm can become a monopsony, which we discuss below.

A monopsonist firm has a labor supply curve _Lit_ ( _wit_ ), that is increasing with wage level _wit_ . Short-run profit maximization, taking the labor supply curve as given, can be expressed as where _Rit_ = _PitQit_ represents total revenues.<sup>11</sup> Maximization with respect to materials yields expression (5) with the the superscript _v_ replaced by _m_ . Maximization with respect to labor yields the first-order condition where _Rit_<sup>_L_representsthemarginalrevenueoflabor,</sup><sup>_γit_=</sup> 1+(( _ε_<sup>_L_</sup> _<u>wε</u>_<sup>_L_</sup> _w_<sup><u>)</u></sup><sup>_<u>it</u>_)</sup><sup>_it_measuresthedegreeof</sup> monopsony power, and ( _ε_<sup>_L_</sup> _w_<sup>)</sup><sup>_it∈R_+isthewageelasticityoflaborsupply.</sup> From the first-order condition in equation (7), the degree of monopsony power is the key variable needed to empirically evaluate whether a firm holds market power in the labor market. To derive the degree of monopsony power empirically, notice that equation (7) can be expressed in terms of elasticity of output with respect to labor as from which it follows that the degree of monopsony power can be measured directly from the production data as Accordingly, the joint parameter of market imperfection can have three cases depending on the labor market setting. That is, The main elements needed to compute markups, the joint parameter of market imperfection, and degree of monopsony power are _α_<sup>_v_</sup> , and _θ_<sup>_v_</sup> of the production inputs. While 10
**Table 1: Descriptive statistics by world region (2005-2006)**

|**Stt**|**O**|**utputs (x100**<br>|**0)**<br>||**Inputs**||
|---|---|---|---|---|---|---|
|**a.**|**Passenger**|**Tons of**<br>**freight**|**Aircraft**<br>**movements**|**Employees**|**Runways**|**Boarding**<br>**bridges**|
|||**LAC (22 air**<br>|**ports, 44 obse**<br>|**rvations)**<br>|||
|**Mean**|6,430.6|117.2|96.1|424.0|1.5|11.3|
|**STD**|6,033.6|119.0|82.9|412.0|0.5|9.7|
|**Min.**|181.0|0.2|1.9|20.0|1.0|0.0|
|**Max.**|24,727.0|470.9|356.0|1,568.0|2.0|38.0|
|||**ASIA (23 ai**|**rports, 39 obse**|**rvations)**|||
|**Mean**|18,776.7|836.0|148.2|1,044.0|1.7|52.3|
|**STD**|12,432.4|970.7|82.7|1,107.3|0.6|35.5|
|**Min.**|1,293.3|10.3|10.5|137.0|1.0|0.0|
|**Max.**|45,100.0|3,600.0|286.5|4,873.0|3.0|143.0|
||**E**<br>|**urope (40 a**<br>|**irports, 66 obs**<br>|**ervations)**<br>|||
|**Mean**|19,305.0|318.3|211.8|2,029.4|2.3|67.9|
|**STD**|15,728.4|515.7|127.8|2,982.6|1.0|58.3|
|**Min.**|1,218.9|3.6|29.8|298.0|1.0|6.0|
|**Max.**|67,915.0|2,131.0|533.0|17,528.0|6.0|264.0|
||**Can**|**ada & US (6**|**3 airports, 125**|**observations)**|||
|**Mean**|21,318.4|406.5|310.9|549.9|3.4|69.9|
|**STD**|17,976.6|641.8|196.7|480.7|1.2|42.6|
|**Min.**|2,657.1|3.6|60.5|119.0|1.0|14.0|
|**Max.**|85,907.4|3,713.4|980.4|3,000.0|7.0|178.0|

The computed Technical Efficiency (TE) scores for airports in the four regions are presented in Table 2.<sup>6</sup> The average TE score of airports in all regions is 0.545 under the constant returns to scale (CRS) assumption. This means that, on average, the airports included in the sample are half technically efficient or, in other words, that they could almost double their outputs using the same quantity of inputs.

However, part of the distance to the best practice CRS frontier is explained by the scale of operation. Under the variable returns to scale (VRS) assumption the average TE is 0.629. Therefore the average scale efficiency (SE) is 0.875. Moreover, for each scale inefficient airport it is possible to identify the type of scale inefficiency: either increasing or decreasing returns to scale, denoted in Table 2 as IRS and DRS, respectively. In the last three columns of Table 2 we report the percentage of airports corresponding to this classification. Grouping all regions,

> 6 All DEA computations, including Malmquist indexes presented in Section 5, were performed using the DEAP program developed by Coelli (1996).

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is not significant (t(157.338) = 0.535; p = 0.296). Shippers who master their logistics seem the most efficient. The information appears to be the key to improve performance (Figure 19). These findings hold for Kenya, South Africa, and, to a lesser extent, Uganda (Figure 20).

**Figure 19: Cargo Dwell Time, by Level of Information about the Clearance Process Provided by C&F Agents, 2011**

<!-- Start of picture text --> 12<br>9.75 9.82<br>10 8.86<br>8.03<br>8<br>6<br>4<br>2<br>0<br>no info until cargo cleared & some info but no reliable clearance delays well accurate forecast & real time<br>delivered clearance delay documented in advance but info<br>unxpected events might<br>hamper progress<br><!-- End of picture text --> _Source:_ Authors based on firm surveys.

**Figure 20: Cargo Dwell Time in Select African Countries, by the Level of Information about the Clearance Process Provided by C&F Agents, 2011**

<!-- Start of picture text --> no info until cargo cleared & delivered<br>some info but no reliable clearance delay<br>clearance delays well documented in advance but unxpected events might hamper progress<br>accurate forecast & real time info<br>15<br>10<br>5<br>0<br>Kenya Nigeria South Africa Uganda Zambia<br>Source: Authors based on firm surveys.<br><!-- End of picture text -->

# **Main Determinants in the Selection of C&F Agents**

When shippers select C&F agents based on their professionalism, cargo dwell time is likely to be shorter than in the case of a selection based on the long term relationship with the agent (Figure 21). Hence costs remain the selection criterion that reflects in the lowest dwell time in the Sub-Saharan African countries analyzed.<sup>10</sup> However, the picture is not clear when looking at the data by country and may reflect a misunderstanding of the questions asked (Figure 22).

> 10 T-tests are not shown here since none of them is statistically significant.

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There is a very consistent pattern that arises from the three data series. The average growth rate in civil war countries during peace gets very close to the average growth rate of peaceful countries. However, these countries grow much more slowly during violent periods. In other words, on average they would not fall behind economically if their recovery period was long enough. In this view it is not recovery in itself that is the problem but the fact that it does not last.

Since this is a core finding we propose several additional ways to illustrate the importance of fragility. First, we develop a measure for the fragility of peace. One, extremely simple, way is to calculate: which provides a number between 0 and 1. This number indicates the likelihood that a year of peace is followed by another year of peace. If this number is high it means that peace in the country was very persistent. Low persistence is an indicator of fragility. Since the calculation of persistence relies on rare events (the start of an armed conflict) it will vary considerably regarding to the sample period and definition of conflict used. In order to include as much violence as possible we use data on all internal PRIO armed conflicts since the second world war to calculate the persistence of peace. Figure 6 shows how persistence of peace, defined this way, relates to GDP per capita now. There is a striking positive relationship. Countries with a high persistence of peace have a much higher GDP per capita than countries with low persistences. This relationship holds despite the fact that we excluded a set of relatively rich countries which never experienced armed conflict.

We use the persistence of peace to explore country variation in GDP per capita. We run a regression, not reported here, of log GDP per capita in 2013 on the persistence measure.<sup>15</sup> The persistence of peace and log GDP are strongly associated: the estimated coefficient of the persistence measure is around 4 and it is statistically significant. A coefficient of 4 suggests that an increase of persistence of 10 percentage points is associated with an increase of GDP by about 50 percent. To have a practical example of what this relationship means, consider the case of Ethiopia from Figure 6. This country has a persistence of peace of only 0.5. If we interpret the results here as causal, the change from 0.5 to a value close to 1 would be associated with 6-fold increase of GDP per capita.

A stable recovery is so important for economic development that this fact can even be made visible in a much simpler way. Figure 7 Panel (a) shows GDP per capita in 2014 and the conflict history of countries which have experienced at least one year of violence. The y-axis captures the log of GDP per capita in 2014 while the x-axis shows the number of years a country has experienced high intensity violence as defined by our

> 15We exclude countries that never had an armed conflict. We also exclude countries with less than 1 million inhabitants.

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<!-- Start of picture text --> Colombia Uruguay<br>Bonilla Hincapie Cerdan-Infantes, Lambi<br>(2009) (2005, 2009) Vermeersch (2013)<br>(2007)<br>4 £ g !<br>5 ® o w 5 ® 5 é<br>$b mH6g 5ga3) 5c 8 [a]5 8<br>= ® - 8 4 »®<br>8 = 8 = § = 28<br>P| Insignificant P| Significant and negative P| Significant and positive<br><!-- End of picture text -->
# **Refugees and Humanitarian Aid: The Rohingya Impact on Bangladesh’s Development**<sup>*</sup>

Alvin Etang Ndip<sup>†</sup> Sandra V. Rozo<sup>‡</sup> Mar ́ıa Jos ́e Urbina<sup>§</sup> Christina Wieser<sup>¶</sup>

**Keywords:** Refugees, Deforestation, Labor Market **JEL Classification:** J1, J6, O1

*We thank Zaeem Al Ehsan for his work as field coordinator of the Cox’s Bazar Panel Survey (CBPS) and for cleaning and preparing the data for analysis, Data International for undertaking the data collection, and Roberto Mendoza for his excellent work processing the remote sensing data. We also thank our partners at Yale University including Mushfiq Mubarak, Paula Lopez Pena, and Ellen Bates-Jefferys, and George Washington University including Jennifer Seager, for the excellent collaboration on the CBPS. We also appreciate the comments and suggestions of Ximena Del Carpio, Abdoulaye Seck, Gayle Martin, Souleymane Coulibaly, Ayago Wambile, and Rumana Islam. Several Bangladesh Country Office World Bank staff also provided useful comments on preliminary findings that helped to strengthen the paper. This project was approved by IRB protocol #2000033048. The CBPS data collection was generously funded by the World Bank-UNHCR Joint Data Center on Forced Displacement. Rozo acknowledges support from the Research Support Budget from the DEC Vice Presidency. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent. All errors are our own.

> †World Bank, Poverty and Equity Global Practice. E-mail: aetangndip@worldbank.org

> ‡World Bank, Dev. Research Group. Corresponding author, e-mail: sandrarozo@worldbank.org

> §World Bank, Dev. Research Group. E-mail: murbinaflorez@worldbank.org

> ¶World Bank, Poverty and Equity Global Practice. E-mail: cwieser@worldbank.org
asset leased, by lowering their financing costs or reducing their spread between funding cost vis-a-vis lease discount factor.

13. For the lessee the chief advantage that lease financing presents is a significantly lower discounted present value of cash disbursements over the term of the lease compared to the discounted present value of payments associated with bank-financed acquisition of an asset. The reason is that the aggregate periodic lease rental payments, which are a combination of interest-related financing costs and payments against principal, can be booked by the lessee as a business expense to shield against tax liability on income realized. The asset financed through a lease is depreciated over the life of the lease, a period shorter than its economic life.

14. On the other hand, the depreciation expense shield against tax liability in an asset acquisition financed by a conventional bank loan is for a longer period (the prescribed useful life of the assets) and a correspondingly smaller amount. If the asset to be acquired has a useful life of say, 5 years, a four-year lease financing arrangement effectively depreciates the asset over 4 years. In contrast, conventional bank loan financing for a similar four-year term will only provide an 80% tax shield (= 4 years depreciation) during the term of the loan.

_15._ Table 1 below summarizes the comparative advantage of lease financing, using a lease discount factor of 18%, over conventional bank financing at an annual percentage rate of 18%, for acquiring a P. 200,000 asset over a four-year term.<sup>3</sup> The present value analysis in Table 1 below shows lease financing with a 17.5 % advantage over outright purchase of the asset through bank loan financing, when the impact of the tax shields is a prime consideration. When the tax shield is not an important consideration, or is irrelevant for the lessee (as might be the case for owners of MSBs), the discounted present-value advantage of leasing over conventional bank financing is much smaller at F. 3,952 or a difference of only 2.1 % over the relevant time period.

3The numerical example is based on a hypothetica **l** ease using Philippine tax rates and lease contract rates of BPI Leasing, as discussed with Mr. Cesar Tejada, President - BPI Leasing Corporation (Philippines), November 5, 1996.

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dimension: where the regulatory framework is not competitive with other countries, reforms should be undertaken. The DB studies can help governments to identify reform areas based on their countries’ performance as compared to other countries. Table 1 lists the nine core indicators and their constituent components.

**<u>Table 1:</u>** <u>The Doing Business Indicators</u>

|<br>**1.**<br>**Starting a**<br>Procedures<br>(number)|<br>**4.**<br>**Getting**|Legal Rights Index|**7.**<br>**Trading**|Documents for<br>export(number)|
|---|---|---|---|---|
|**Business**<br>Time (days)|**Credit**|Depth of Credit<br>Information Index|**Across**<br>**Borders**|Time for export<br>(days)|
|Cost (% of income<br>per capita)||Public registry<br>coverage<sup>*</sup>||Cost to export<br>(US$ /container)|
|Min. capital (% of<br>incomeper capita)||Credit bureau<br>coverage<sup>*</sup>||Documents for<br>import(number)|
|**2. Dealing**<br>**with**<br>Procedures<br>(number)|**5.**<br>**Protecting**|Disclosure Index||Time for import<br>(days)|
|**Licenses**<br>Time (days)|**Investors**|Director Liability<br>Index||Cost to import<br>(US$ /container)|
|Cost (% of income<br>per capita)||Shareholder Suits<br>Index|**8.**<br>**Enforcing**|Procedures<br>(number)|
|**3.**<br>**Registering**<br>Procedures<br>(number)|**6.**<br>**Paying**|Payments<br>(number)|**Contracts**|Time (days)|
|**Property**<br>Time (days)|**Taxes**|Time (hours)||Cost (% of debt)|
|Cost (% of property<br>value)||Total tax rate (%<br>profit)|**9.**<br>**Closing a**<br>**Business**|Time (years)<br>Cost (% of estate)|
|||||Recovery rate<br>(cents on$US)|

* These sub-indicators only indirectly influence the overall DB ranking. Source: World Bank (2011)

Table 1 illustrates that the indicators represent all stages of a business’s life cycle: from its incorporation through its operation, to its closure. Countries are then ranked based on their performance on these nine indicators. In addition there are two indicators that do not persistently form part of the ranking. The first one measured the quality of labor market regulation where regulation was assumed to be better if it was easier for businesses to lay off workers. This indicator proved controversial as labor protection could also be construed as a country’s advantage (e.g. Berg and Cazes, 2007). Indeed, this reason is in line with Hall and Soskice (2001) who argue that labor protection can determine the quality of labor and in return of a country’s exports. Accordingly, this indicator has been taken out of the ranking since the 2011 DB report (published in 2010). The other indicator measures the ease of getting electricity. It was included in the 2011 report as a pilot indicator and was included in the 2012 DB rankings.

Although DB captures a rather comprehensive set of factors influencing regulatory quality it still only captures a fraction of it. The 2011 DB report (World Bank 2011:13) comments on this as follows: 3
standard nutrition counseling and less-intensive mass media and community mobilization (Menon et al. 2016). The lessons learned from initiatives to improve the positive impact of information, education, and counseling interventions on complementary feeding practices in South Asia are presented in box 1.

# **Box 1: Lessons Learned from Initiatives to Improve the Positive Impact of Information, Education, and Counseling Interventions on Complementary Feeding**

- Design  Ensure adequate investment in formative research to inform design

- Contact  Use multiple communication channels (interpersonal communication, community mobilization, and mass media) and multiple delivery platforms to reach caregivers and their influencers (including husbands) with messages and counseling

- Ensure adequate number and duration of contacts, including home visits, between frontline workers and caregivers

- Content  Reflect the most critical issues that caregivers face at each stage of a child’s life in message content and counseling

- Remember that the quality of counseling is at least as important as coverage

- Frontline workers  Define the roles and responsibilities of different cadres

- and performance  Provide quality training, incentives, and supportive supervision

- Monitoring  Institutionalize regular monitoring of both coverage and quality of counseling to inform program changes

- Additional  Complement information and counseling with intervention by

- interventions food and social protection systems, as needed, to increase access to affordable, nutritious food in food-insecure settings

_Sources:_ Avula et al. 2013; Menon et al. 2016; Alive & Thrive 2018 There is increasing recognition that optimal infant feeding depends not only on what and how often a child is fed but also on how he or she is fed (PAHO/WHO 2003; WHO 2005). Feeding is “responsive” if the caregiver recognizes the child’s signals of hunger and satiety and responds promptly in a manner that is developmentally appropriate and supportive. All South Asian countries except Bangladesh and Pakistan have programs at scale that provide education or counseling on responsive feeding and early stimulation, but information on the coverage and quality of these services is absent. Rigorous evidence on the impact of interventions that aim to promote responsive feeding is scarce in South Asia. A communitybased responsive feeding program in Bangladesh increased maternal responsiveness, selffeeding, and the number of mouthfuls eaten by children aged 8–20 months (Aboud et al. 2009; Aboud and Shafique 2011). On feeding during common childhood illnesses, evidence from India suggests that individual and group counseling organized through the primary health system can improve a caregiver’s knowledge and practices (Paintal and Aguayo 2016).

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# **A Prevalence of Prejudice Toward Migrants by Level of Education**

Figure A.1. Prejudice Prevalence by Level of Education Panel A. Colombia <!-- Start of picture text --> At Least One Variable Equals 1 Index Average Index of Variables<br>1 0 .9 4 0 .9 6 0 .9 5 1<br>.9 0.89 .9<br>.8 .8 0 .7 6<br>0 .7 2<br>.7 .7 0.66 0 .6 6<br>.6 .6<br>.5 .5<br>.4 .4<br>.3 .3<br>.2 .2<br>.1 .1<br>0 0<br>Education Level Education Level<br>No Studies Primary Secondary Universitary or more No Studies Primary Secondary Universitary or more<br>Panel B. Latin America (18 Countries)<br>At Least One Variable Equals 1 Index Average Index of Variables<br>1 1<br>.9 0 .8 7 0 .9 0 0.89 0 .8 6 .9<br>.8 .8<br>.7 .7<br>.6 .6 0 .5 8 0 .5 9 0.59 0 .5 9<br>.5 .5<br>.4 .4<br>.3 .3<br>.2 .2<br>.1 .1<br>0 0<br>Education Level Education Level<br>No Studies Primary Secondary Universitary or more No Studies Primary Secondary Universitary or more<br>Prejudice Prevalence Prejudice Prevalence<br>Prejudice Prevalence Prejudice Prevalence<br><!-- End of picture text --> _Notes:_ The figure depicts the average level of prejudice by level of education for both the Colombian population and the aggregate Latin American population. The Prejudice Prevalence variable is constructed using the following indicator variables: (i) Accepting Venezuelan migrants will have a negative impact [=1], (ii) Belief that migrants have a negative impact on the host economy [=1], Belief that migrants compete with hosts for jobs [=1], (iii) Belief that migrants contribute to increased crime [=1], and (iv) Belief that migrants impose a burden on the state [=1]. The At Least One Variable Equals 1 Index equals 1 if any of the aforementioned variables is 1, and the Average Index of Variables is the mean of these variables. The data is extracted from the Latinobarometro survey for the year 2020, which is an annual public opinion survey representing the populations of 18 Latin American countries.