rafmacalaba/datause-encoder-data huggingface dataset:
|**Shipping route**|**# of calls/month**|**Average vessel size**|**Schedule**|
|---|---|---|---|
|Mainline Eur-Af-Eur|18|826|Variable|
|Mainline Asia-Africa-Asia|13|1 899|Variable|
|Feeder intra Af|6|510|Variable|
|Am-Eur-Af|4|624|variable|
|Mainline Af-Med-Eur-Med-Af|4|2 474|fixed|
|Med-Af-Med|4|2 248|fixed|
|Feeder Af-Eur-Af|3|711|variable|
|Mainline IndSub-ME-Af -IndSub|3|1 903|variable|
|Am-Af|2|800|variable|
|Mainline intra Af|2|1 608|variable|
|Af-Med-Af|1|188|variable|
|Feeder Asia-Af-Asia|1|954|variable|
|Mainline North Am-Eur-Af|1|925|variable|
|**Total**|**62**|**1 205**||

**Table 10 – Regular calls in the port of Douala** (Source: Containerization international – 2010)

**Contracting patterns** of C&F agents also exhibit some revealing peculiarities. The introduction of a time efficiency indicator with a weight of 30% in the national evaluation framework of C&F agents ( _Label Qualité des Commissionnaires Agréés en Douane_ ) tends to prove that there is a good awareness of the importance of time efficiency in the satisfaction of shippers. However few shippers actually manage to introduce compelling time efficiency terms in the contracts with their C&F agents, especially dominant C&F agents that have a very strong supplier power. Those shippers that manage to include performance conditions in their C&F contracts actually formulate them in a way that leaves room for argument (e.g. maximum clearance time to the condition that all documents are submitted correctly and in a timely manner by shippers). This is why the largest brokers maintain very high market shares despite poor time performance.<sup>27</sup> There are good reasons to believe that the wider recognition of the national brokers’ label will slowly increase requirements towards customs brokers but shippers would have to envisage replacing brokers that they have contracted for years. This seems improbable due to very strong straight re-buy patterns (loyalty of shippers).

Another major issue is the **cash availability issue** and shippers’ strategies to reduce financial exposure. Because of costly trade loans and limited import financing tools shippers are often short in cash in their daily operations and this is a major hindrance to the reduction of dwell times. In Table 3 we show the bulk of customs declaration lodging is done in the second or third week after container discharge, while customs clearance process does not take more than 3 days in average. The first potential saving in first step that takes 13 days in average today and a key element to achieve shorter

> 27 See table 9.

21
eerBlack Hisp. White Black Hisp. White Baseline Note: “Baseline”Noshows Chartersaverage Fitignproficiency index[520for students|of each009demographic| 2mgroup[028and grade level[| (inonpercent).|“NoonCharters”| shows the difference in average proficiency index between Baseline and No Charters (in percentage points). “Elementary” refers to K-6 grades; “Middle” to grades 7 and 8, and “High” to 9-12 grades.

eeBlack Hisp. White Black Hisp. White Baseline No Charters Note: “Baseline” shows the average welfare for students of each demographic group and grade level. “No Charters” shows average compensating variation from the counterfactual for student each demographic group and grade level (negative values are in parentheses). “Elementary” refers to K-6 grades; “Middle” to grades 7 and 8, and “High” to 9-12 grades.
# Table 4: **Regression Discontinuity** – First Stage

This table reports evidence on the program-induced discontinuity in debt relief, based on bank data. The table additionally reports placebo tests using total eligible amount, eligible principal amount and eligible overdue interest. _Debt Relief_ refers to the net amount of debt relief granted. _Eligible Amount, Total_ (rupees) refers to the total exante overdue balance to which the program criteria were applied and is the sum of _Eligible Amount, Principal_ and _Eligible Amount, Interest_ . Coefficient estimates are obtained from regressions on treatment status and a local linear control function, using the sample of all surveyed households. Robust standard errors, in brackets, are clustered by bank and district. + p _<_ 0.10 ** p _<_ 0.05 *** p _<_ 0.01

||Coeffcient|_SE_|_N_|
|---|---|---|---|
|Implemented Debt Relief||||
|All Banks|37,156***|[1,858]|2,897|
|Commercial Banks|44,037***|[3,455]|1,475|
|Cooperative Banks|34,339***|[1,925]|1,422|
|Eligible Amount, Total||||
|All Banks|-2,939.7|[3,843]|2,442|
|Commercial Banks|-5,599.5|[5,603]|1,348|
|Cooperative Banks|4,186.5|[4,285]|1,094|
|Eligible Amount, Principal||||
|All Banks|-4,517.2|[3,225]|2,443|
|Commercial Banks|-4,850.4|[4,996]|1,349|
|Cooperative Banks|1,432.4|[2,483]|1,094|
|Eligible Amount, Interest||||
|All Banks|1,446.8|[1,442]|2,418|
|Commercial Banks|-1,414.8|[1,509]|1,325|
|Cooperative Banks|3,375.8|[2,403]|1,093|

35
have been targeted to areas that would have experienced growth anyway. Following Alder et al. (2018), we address this endogeneity concern by controlling for pre-trends in urban growth and luminosity. In addition, following Donaldson and Hornbeck (2016), Blankespoor et al. (2017), and Jedwab and Storeygard (2016), we implement a doughnut instrumental variables (IV) strategy. Here, we instrument market access with an alternative version of market access that excludes units within a certain buffer. Specifically, when calculating market access for each unit, units within a specified buffer are excluded. For our primary model, we use a 50km buffer, but we also test 20km and 100km buffers. This instrument helps address endogeneity concerns by excluding units more prone to non-random road placement (Blankespoor et al., 2017). When including interaction terms with market access, we instrument using the interaction of the “doughnut” market access variable and the interaction terms. For example, we instrument _MA_ and _MA × Distance Addis_ with _MAdoughnut_ and _MAdoughnut × Distance Addis_ .

To take full advantage of the panel nature of our data, we test the robustness of the long-difference results using a two-way fixed effects model. This approach follows Blankespoor et al. (2017), who measure the impact of road improvements in Mexico. The methodology is discussed in more detail in Appendix S12.

# **5 Results**

## **5.1**

Figures 4 and 5 show results after recovering the average treatment effect by length of exposure to road improvements using Kebeles as the unit of analysis. We find that road upgrades contributed to growth in both nighttime lights and urban land, and a decrease in cropland. Urban areas grow immediately after the upgrades, while nighttime lights growth takes place five years after the upgrades. Most of the impact is driven by proximity to roads with higher speed limits, but this might be the result of selection of locations into treatment. In addition, pre-treatment coefficients generally appear flat throughout all results, suggesting no-to-minimal pre-trends.<sup>10</sup> Comparing places with different initial luminosity levels, coefficients on road improvements for nighttime lights show relatively similar magnitudes in areas with low and high baseline nighttime lights; road improvements lead to approximately 10–20% growth in nighttime lights. In models with urban land as the dependent variable, the coefficient on road improvements is notably larger in areas with high initial nighttime lights compared to areas with low initial lights. Results using only two groups of baseline nighttime lights show similar results, with gains concentrated in Woredas with positive nighttime lights (see Appendix S13).

> 10Across most regressions, the p-value of the Wald pre-test of the parallel trends assumption is generally near zero; however, only a small number of pre-treatment coefficients appear significant in any regression, and the coefficients are small in magnitude.

19
ratings have improved, giving Latvia easier and less expensive access to international capital markets.

**<u>Table 4: Tax Burden and Effective Tax Rates (2002 or latest year available)</u>**

||Non-wage<br>labor costs|**Effective Tax Rate**<br>Personal income|**(in percent o**<br>Capital|**f tax base)**<br>Consumption|**Tax receipts**<br>**(percent of GDP)**|
|---|---|---|---|---|---|
|Latvia|n.a|25 percent|n.a|18 percent VAT; excise<br>tax|29.6|
|Germany|31.8|17.8|15.9|17.9|43.1|
|France|32.1|15.2|22.6|24.5|46.4|
|United States|11.6|13.9|22.7|9.3|29.9|

For the medium term, Latvia is committed to further fiscal consolidation accompanied by some relief in the tax burden, which is still high relative to several western countries (Table 4). The main question at this stage is what strategy and what specific policy instruments the authorities should adopt to reach their goal of a balanced budget. Lessons from the previous wave of EU accession countries of the 1970s and 1980s (notably Greece, **Figure 9: The Fiscal Cost of EU-Accession (as percent of GDP)** Ireland, Spain, and Portugal) suggest that Latvia will face some major fiscal 6 challenges in the medium term as it 5 reconciles several conflicting policy choices: Specifically, the country will 4 have to accommodate additional public 3 expenditure ( _acquis_ -related spending and an increase in its military budget as 2 part of NATO membership) while 1 complying with tax harmonization requirements, pursuing its lower direct 0 tax rates policy, and making efficient 2002 2003 2004 2005 2006 use of transfers from the EU that will generate pressure on the size of government. In order to carry out these new responsibilities without diverging from the path of a balanced budget, further rationalization of public expenditure and reduction of non-priority spending will be required.

**Figure 9: The Fiscal Cost of EU-Accession (as percent of GDP)**

<!-- Start of picture text --> 6<br>5<br>4<br>Grants from EU<br>3<br>EU-related expenditure<br>2<br>1<br>0<br>2002 2003 2004 2005 2006<br><!-- End of picture text --> Projections of the likely financial flows to and from the EU budget carried out by the Mueller et al. (2002) indicate that under the baseline scenario, EU-related expenditure could average 5.4 percent of GDP in 2004-06 (see Figure 9). Latvia would need a reduction in discretionary spending by about 2 percentage points in the medium term, which could be effected by “moderate wage increases, coupled with civil service reforms, improved targeting of social benefits, and effective cuts of expenditure on goods and services.” Savings will need to be generated in other parts of the government to increase funding for priority programs. More fundamentally, the key challenges for the government are first to develop and use a (proper) methodology to make appropriate choices among policy priorities and, second, to allocate its resources to those priority areas in a manner conducive to sustainable, cost-effective results.

As an integral part of Latvia’s fiscal policy for the medium term, expenditure policies could make an important contribution to growth and convergence. A recent World Bank report on 22
Figure A4: Stringency of lockdown restrictions and Google mobility trends around transit stations. Selected countries.

<!-- Start of picture text --> BGR BRA CIV<br>100 20 100 20 100 20<br>80 0 80 0 80 0<br>60 -20 60 -20 60 -20<br>40 -40 40 -40 40 -40<br>20 -60 20 -60 20 -60<br>0 -80 0 -80 0 -80<br>01/01 01/02 01/03 01/04 01/05 01/06 01/07 01/08 01/01 01/02 01/03 01/04 01/05 01/06 01/07 01/08 01/01 01/02 01/03 01/04 01/05 01/06 01/07 01/08<br>PAK VNM ZAF<br>100 20 100 20 100 20<br>80 0 80 0 80 0<br>60 -20 60 -20 60 -20<br>40 -40 40 -40 40 -40<br>20 -60 20 -60 20 -60<br>0 -80 0 -80 0 -80<br>01/01 01/02 01/03 01/04 01/05 01/06 01/07 01/08 01/01 01/02 01/03 01/04 01/05 01/06 01/07 01/08 01/01 01/02 01/03 01/04 01/05 01/06 01/07 01/08<br>Stringency of lockdown restrictions<br>Mobility around transit stations<br>Stringency of lockdown measures Google mobility index (baseline = 0) Stringency of lockdown measures Google mobility index (baseline = 0) Stringency of lockdown measures Google mobility index (baseline = 0)<br>Stringency of lockdown measures Google mobility index (baseline = 0) Stringency of lockdown measures Google mobility index (baseline = 0) Stringency of lockdown measures Google mobility index (baseline = 0)<br><!-- End of picture text --> Figure A5: Actual and predicted mobility trends around transit stations. Selected countries.

<!-- Start of picture text --> BGR BRA CIV<br>20 20 20<br>0 0 0<br>-20 -20 -20<br>-40 -40 -40<br>-60 -60 -60<br>-80 -80 -80<br>01/01 01/02 01/03 01/04 01/05 01/06 01/07 01/08 01/01 01/02 01/03 01/04 01/05 01/06 01/07 01/08 01/01 01/02 01/03 01/04 01/05 01/06 01/07 01/08<br>PAK VNM ZAF<br>20 20 20<br>0 0 0<br>-20 -20 -20<br>-40 -40 -40<br>-60 -60 -60<br>-80 -80 -80<br>01/01 01/02 01/03 01/04 01/05 01/06 01/07 01/08 01/01 01/02 01/03 01/04 01/05 01/06 01/07 01/08 01/01 01/02 01/03 01/04 01/05 01/06 01/07 01/08<br>Predicted<br>Actual data<br>Google mobility index (baseline = 0) Google mobility index (baseline = 0) Google mobility index (baseline = 0)<br>Google mobility index (baseline = 0) Google mobility index (baseline = 0) Google mobility index (baseline = 0)<br><!-- End of picture text --> 46
# **Table 5:  Theil index of earnings inequality decompositions**

|**(i) Decomposition of**|**overall e**|**arnings in**|**equality**||||
|---|---|---|---|---|---|---|
|**Total**|**White**<br>|**Nonwhite**<br>|**White**<br>|**Nonwhite**<br>|**Within**|**Between**|
||**Men**|**Men**|**Women**|**Women**|||
|**Bolivia**|||||||
|Observed earnings|||||||
|Theil(1)<br>0.60|0.47|0.53|0.56|0.69|0.53|0.07(12%)|
|Theil(0)<br>0.73|0.52|0.69|0.61|0.82|0.66|0.07(10%)|
|Simulated earnings — re|turns only||||||
|Theil(1)<br>0.51|0.47|0.47|0.54|0.49|0.48|0.03 (6%)|
|Theil(0)<br>0.59|0.52|0.58|0.56|0.64|0.56|0.03 (5%)|
|Simulated earnings — re|turns and|characteristi|cs||||
|Theil(1)<br>0.55|0.47|0.63|0.44|0.62|0.54|0.01(2%)|
|Theil(0)<br>0.65|0.52|0.74|0.55|0.74|0.64|0.01(2%)|
|**Brazil**|||||||
|Observed earnings|||||||
|Theil(1)<br>0.65|0.60|0.51|0.59|0.50|0.57|0.07(11%)|
|Theil(0)<br>0.58|0.56|0.45|0.53|0.44|0.50|0.08(14%)|
|Simulated earnings — re|turns only||||||
|Theil(1)<br>0.63|0.60|0.56|0.62|0.59|0.60|0.03 (5%)|
|Theil(0)<br>0.57|0.56|0.50|0.57|0.52|0.54|0.04(7%)|
|Simulated earnings — re|turns and|characteristi|cs||||
|Theil(1)<br>0.65|0.60|0.63|0.66|0.66|0.63|0.02(3%)|
|Theil(0)<br>0.59|0.56|0.54|0.59|0.57|0.56|0.02(4%)|
|**Guatemala**|||||||
|Observed earnings|||||||
|Theil(1)<br>0.78|0.73|0.55|0.67|0.69|0.69|0.09 (12%)|
|Theil(0)<br>0.86|0.72|0.65|0.85|0.85|0.75|0.11(13%)|
|Simulated earnings — re|turns only||||||
|Theil(1)<br>0.71|0.73|0.51|0.63|0.44|0.67|0.04(6%)|
|Theil(0)<br>0.71|0.72|0.58|0.66|0.63|0.67|0.05(6%)|
|Simulated earnings — re|turns and|characteristi|cs||||
|Theil(1)<br>0.75|0.73|0.86|0.68|1.29|0.75|0.01(1%)|
|Theil(0)<br>0.74|0.72|0.81|0.67|0.93|0.73|0.01(1%)|
|**Guyana**|||||||
|Observed earnings|||||||
|Theil(1)<br>0.33|0.32|0.29|0.41|0.26|0.31|0.02(6%)|
|Theil(0)<br>0.29|0.27|0.27|0.34|0.26|0.28|0.02(5%)|
|Simulated earnings — re|turns only||||||
|Theil(1)<br>0.34|0.32|0.34|0.36|0.41|0.34|0.00 (0%)|
|Theil(0)<br>0.29|0.27|0.30|0.29|0.43|0.29|0.00(0%)|
|Simulated earnings — re|turns and|characteristi|cs||||
|Theil(1)<br>0.33|0.32|0.39|0.27|0.35|0.33|0.00 (0%)|
|Theil(0)<br>0.29|0.27|0.35|0.22|0.37|0.29|0.00(0%)|
in areas with a particularly bad quality of governance. The negative effects of corruption are also less pronounced in low-income regions.<sup>16</sup>

# **The Role of Courts**

Courts are an important institution for property rights protection. A sound legal system is a prerequisite for the Coase theorem to work its magic—for transacting parties to reach social optimum (Coase 1960). A good court system would sustain firms’ expectation that their contractual rights would be honored in the face of contract breach, allowing them to commit necessary investments and to expand without worrying about contract reneging.

The legal system helps firms grow by improving the trust needed for new transaction relationships (Johnson et al. 2002). Using firm data from Poland, Romania, Russia, and Slovakia to examine how court quality affects business relationships, Johnson et al. find that beliefs in the effectiveness of courts are associated with a higher level of trust shown in new relationships between firms and their customers. Well-functioning courts also encourage entrepreneurs to try out new suppliers, which facilitate new entry and firm expansion. Courts are found to be particularly important when specific investments are needed for a relationship to develop—and specific investments become more important as the economy develops since there are increased demands for customized as opposed to standard goods.

The legal system also reduces the reluctance to expand and increases firm size where owners cannot effectively diversify their idiosyncratic risks. Using aggregated firm census data in 1998 and focusing on within-Mexico cross state-industry variations in firm sizes and the quality of local legal institutions, Laeven and Woodruff (2008) find that firm size increases in the quality of the local legal system. Furthermore, the legal system affects firm size by reducing the idiosyncratic risk faced by firm owners—the legal system has a smaller impact on partnerships and corporations than on proprietorships, where risk is concentrated in a single owner. They deal with the potential endogeneity of the quality of the local legal system by instrumenting it with historical conditions (the share of the indigenous population in 1990 and the number of cultivated crops with large scale economies, i.e., sugar, coffee, rice and cotton, in 1939). The results are also robust with respect to alternative size measures and institutional measures.

> 16 Relatedly, firm performances in China are significantly lower in locations and industries with a higher share of informal payment to sales for firms (Hallward-Driemeier et al. 2006). The effects of corruption in China are likely under-estimated due to the attenuation bias of measurement errors—the incidence of corruption in that data is far too low to be plausible.

16
- Il -

developed financial markets, the financial assets of these institutions are not more than 18 percent of GDP, while in countries with less developed financial markets, such as Greece, Italy, Turkey, and Hungary, non-life insurance financial assets represent less than 5 percent of GDP in 1996.

**Figure 3: Non-life insurance financial assets (GDP %, 1996)**

<!-- Start of picture text --> 20S 1<br>18%.<br>16%<br>14%<br>12%<br>10%<br>I~~~ t 3 g B<br><!-- End of picture text -->

**Source:** 1998 OECD Institutional Investors Statistical Yearbook and WB institutional investors database.

Table 1 shows how the vast majority of personal sector financial assets, across selected countries in 1990, is held in liquid financial assets like cash and deposits. Instead, Figure 4 shows that contractual savings tend to hold a smaller share of financial assets in cash and deposits and a larger share of these assets in stocks, loans, and bonds. Notice how during the 1982-96 period, cash and deposits never exceeded 6 percent of total financial assets. Bills and bonds decreased from about 50 percent to about 43 Table **Tablew** 1:w Asset **vstrucoitur of personal sector,** 1990ilases ars percent, and shares increased from about IS percent to around 30 percent at the expense selecte cofnre **total** shl i iuiiacilaseslk **cash and deositr.** assets, have declined their participation from about 25 percent in the early period to about 15 in the latter.

|**assets**|**Equity**|**Bonds**<br>**deposi**|**Loans**<br>**ts**|**s**|**ons**|
|---|---|---|---|---|---|
|**Japan**<br>|13|5|0|53|23|
|**France**<br>|34|3|o|51|12|
|**Italy**<br>|22|18|o|49|12|
|**Germany**|6|18|0|4 8|2 2|
|**Canada**<br>|21|6|2|3 9|2 8|
|**Australia**|17|13|**o**|34|36|
|**USJap**|19|1|1|30|33|
|**UK**|12|4|0|29|47|
|**Netherlands**|6|8|0|2 9|54|

**Source:** Davis 1995.
# **Declining Wages for College-Educated Workers in Mexico:  Are Younger or Older Cohorts Hurt the Most?**<sup>**1**</sup>

Raymundo M. Campos-Vazquez<sup></sup> Luis F. Lopez-Calva<sup></sup> Nora Lustig<sup></sup>

**Keywords** : Education; College; Cohort; Inequality; Mexico.

**JEL** : I24; I25; J20; J31; O54.

> 1 The authors are grateful to Alma Santillan for her excellent research assistantship.

>  El Colegio de Mexico, Camino al Ajusco 20, Col. Pedregal de Santa Teresa, 10740, Mexico DF, Phone: +52-5554493000, ext. 4153, email: <u>rmcampos@colmex.mx.</u>

>  World Bank.1818 H Street NW, Washington DC, 20433. Phone: +1-202-477-1234. Email: <u>lflopezcalva@worldbank.org.</u>

>  Tulane University, Department of Economics, 204 Tilton Hall, New Orleans LA, 70118, Phone: +1-504-8628347. Email: <u>mlustig@tulane.edu.</u>
links with existing partners, rather than the emergence of large and fast-growing investments with new countries.<sup>21</sup>

# **5. Extensions**

In this section, we further examine whether the patterns documented so far are a general phenomenon across the South or driven by a subset of South countries. Specifically, we analyze the extent to which China or, alternatively, the richest South countries drive our results. In addition, we evaluate the relative size of OFCs, which have been excluded from the analysis so far, to obtain a more complete picture of the patterns of international investment around the world. Each extension considers a third group of countries, expanding the number of blocks in the analysis from 2x2 to 3x3.

First, the rise of the South is not merely driven by China, which has become a key player in global finance. Whereas China substantially expanded international investment across all types from 2001 to 2018, the overall patterns documented in sections 3 and 4 also hold for the other South countries (table 10, panel A). For example, China’s FDI to the North and the rest of the South expanded from 0.1 and 0.6 percent of world GDP, respectively, in 2001 to 0.6 and 2.1 percent in 2018. At the same time, China has become a large recipient of international investment. FDI from the North doubled between 2001 and 2018, increasing from 0.3 to 0.6 percent of world GDP. Despite the growing weight of China in global finance, the rest of the South also grew as a source and destination of international investment, especially vis-à-vis other South countries. For example, FDI within the South (excluding China) increased by 420 percent from 2001 to 2018 (from 0.5 to 2.6 percent of world GDP), outpacing the growth of its FDI to China (up 160 percent) and the North (up 137 percent).

> 21 This result is consistent with earlier findings using a sample of 18 countries for 1980–2005, showing that the international financial network is characterized by a few country pairs with large links and many country pairs with small links (Kubelec and Sa 2012).

25
TABLES & FIGURES

# **Table 1 – Frequency of the rainfall shocks depending on the threshold chosen to define the shock**

|**Shock definition threshold**||Year||
|---|---|---|---|
||2002|2005|2008|
|**Half a standard deviation**||||
|R > mean + 0.5 standard deviation|0.22|0.18|0.61|
|R< mean - 0.5 standard deviation|0.34|0.29|0.02|
|mean - 0.5 standard deviation < R < mean + 0.5 standard deviation|0.44|0.53|0.37|
|**One standard deviation**||||
|R > mean + 1 standard deviation|0|0.09|0.5|
|R< mean - 1 standard deviation|0.16|0.09|0|
|mean - 1 standard deviation < R < mean + 1 standard deviation|0.84|0.82|0.5|
|**Two standard deviations**||||
|R > mean + 2 standard deviations|0|0|0.28|
|R< mean - 2 standard deviations|0|0|0|
|mean - 2 standard deviations < R < mean + 2 standard deviations|1|1|0.72|

22