rafmacalaba/datause-encoder-data huggingface dataset:
# **4.4 Structure of the error terms**

The distance function estimated is stochastic. For the purpose of estimating equation 8, it is necessary to determine the random disturbance term. We applied the methodology developed by Battese and Coelli (1988) for panel data and apply an additive term as suggested by Cuesta and Orea (2002), to account for the fact that we are estimating an output oriented distance function. The error terms thus has the following form: _vit_ + _ui_ (9) where, _vit_ is a symmetrical error term, iid with a zero average (which represents the random variables un-controllable by the operator) and _ui_ is a one-sided negative error term (which measures the technical inefficiency of each operator that is constant over time) and is distributed independently of _vit_ .

Applied to the distance function, this yields This equation can be estimated by the maximum likelihood method which requires _2_ distributional assumptions on the random shock. This assumes that _vit_ follows a _N_ (0, _σv_ ) 2 distribution and _ui_ follows a │ _N_ (0, _σu_ )│distribution (Ritter y Simar, 1997).

This model thus assumes that the inefficiency effects are constant over time. To be able to assess the effects of policy changes on inefficiency levels, we structured the time horizon in 3 periods and considered the port authorities to be independent across periods. The three time periods are: (i) before the reform (1990-1992), (ii) after the first reform (1993-1997) and (iii) after the second reform (1998-2002). This way any change resulting from reform can be assessed within the period.

# **5 The estimation**

# **5.1 The data**<sup>**2**</sup>

The heterogeneity of activities developed at ports and the diversity of commodities handled suggests narrowing the study to a limited number of ports and a specific type of cargo. Following the foregoing recommendation, this study centers its analysis on Spanish ports particularly relevant from the point of view of container traffic.

Statistical information has been gathered from the data published annually by port authorities in their Annual Reports. Furthermore, information from the Statistical Yearbooks released by State Ports –the central agency entrusted with the coordination of the Spanish port system- has been used. Whenever anomalies or discrepancies were encountered, we resorted directly to the source and asked for additional clarifications - whether in person or on the phone- from the people responsible of each field.

The ports included in the sample coincide with the major commercial ports of the country and capture a broad typology of ports: insular ports (with high level of captive traffic), hub ports (relevant at the international level for their importance as merchandise distribution centers, such as the Port of Algeciras), and different specializations (for instance, Santa Cruz de Tenerife and Bilbao in liquid bulks, Alicante in dry bulks, etc). Furthermore, all waterfronts of the Spanish coastline are represented by the ports selected.

> 2 For more details about the data and variables see González, M. (2004).

11
28 Table 7: Instrumental variables eslimates of the participation-performance relationship, using various instrument sets

|Estimation<br>technique/<br>Instrumentset|OLS|'Extent Part. a<br>goalJ|*% of Invest-<br>ment costs by<br>users'|'Net benefits<br>of<br>participation'|'Organization based on<br>local collectives'|Pnor<br>commitment of<br>clients'|All<br>[<br>_|
|---|---|---|---|---|---|---|---|
|Bi|variate||||||<br>|
|coeff<br>(t stat)|.63<br>(10.6)|.70<br>(10.2)|_.59_<br>(7.3)|.77<br>(10.6)|.74<br>(6.3)|.97<br>(7.54)|.86<br>(10.4)|
|N|120|120|113|120|98b|105|90|
|R-squared|.488|.482|.476|_.453_|_*5 0 7h _|.378|.521|
|First stage R-<br>|-|.763|.573|.701|.364|.326|.816|
|squareda<br>Li<br>|mited Multivari<br>|ato_<br>|||_|.||
|Coeff<br>(t stat)|.28<br>(5.25)|.34<br>(5.2)|.32<br>(3.6)|_.36_<br>(5.4)|_.15_<br>(1.28)|.39<br>(3.00)|.37<br>(3.57)|
|N|77|77|75|77|66|72|63|
|R-squared|.862|.860|.861|.858|_.855_|.863|.865|
|First stage R-<br>**squared'**|[.401'1|.826|.643|.803|.719|_.559_|.857|

Notes: a) Unadjusted R-squared of the 'first stage' regression of participation on the instruments (which in the multivariate case includes all variables in the performance equation). b) Since the sample sizes are not the same the results are not strictly comparable in all columns. In particular the IV R-squared are less than OLS R- squared when _run for_ tbe same sample.

c) This is tho R-squared of participation regressed on all the Z variables which are included in the perforrnanc **e** quation. The incremen **t** o the R- squared for each instrument can be calculated as the difference with this column.
the dwelling can also be the largest liability of the household, with implications for its financial vulnerability. The implications for financial stability of rapidly rising household indebtedness and the exposure of banking industries to vulnerable households and “risky” borrowers are causes for concern. The poverty and social implications of a rising debt burden can be enormous, especially in the event of a significant economic slowdown, credit tightening or a macroeconomic crisis.<sup>3</sup> The adverse welfare implications for households of rising debt burdens for households may be further compounded by rising energy and food prices, as we saw in 2007/2008.<sup>4</sup> The costs and benefits of rapidly increasing mortgage holdings and household indebtedness can be compared to a similar debate on financial sector deepening in general. While financial deepening is associated with faster economic growth and reduction in poverty levels (Beck, Levine and Loayza, 2000; Beck, Demirguc-Kunt and Levine, 2007),5 rapidly increasing credit levels have, at the same time, been found to be good predictors of crises (Demirguc-Kunt and Detragiache, 1998). The rise in household lending is also related to the process of financial deepening. As financial systems deepen and economies develop, typically a larger share of bank lending goes to households as opposed to enterprises (Beck et al., 2009 a,b).

To date, little is known about the incidence of household indebtedness and its distribution in the new EU countries. Although some institutions have called for greater use of micro data to assess household indebtedness and overall financial stability, current assessments of the financial risks faced by the banking sector have been largely based on macroeconomic data, including in many advanced

> 3 A significant share of household debt in the new EU countries for which data are available are foreign currencydenominated and have adjustable interest rates, exposing them to exchange rate and interest rate shocks.

> 4 In fact, a new World Bank regional study has just been completed precisely to investigate the macroeconomic risks faced by countries in Eastern and Central Europe and the likely welfare consequences should the risks materialize, with special emphasis on an assessment of household indebtedness (World Bank, 2009).

> 5 A strand of the literature has also documented the welfare consequences of constrained access to credit. See for example Kang and Sawada (2008) and  Sawada, Nawata, Ii, and Lee (2007).

3
10 Within manufacturing, developing country export growth was especially rapid in machinery and transport as well as chemicals. Practically all developing country groups shared in this growth (Table 5) but it was especially rapid in the upper middle income countries—even excluding China, as well as the BRICS and Latin America.

**Table 5: Growth in Sectoral Exports by Income Group and Region during 1980-2010**

||||Annual A|verage Gr<br>|owth Rate in<br>Of<br>|Exports(%)<br>which in Tot<br>|al Manufacturin<br>|g<br>|
|---|---|---|---|---|---|---|---|---|
||Agric. &|Ores &||All|Chemical|Textiles &|Machinery|Other|
|CountryGroup|<br>Feed|Metals|Fuels|Manuf.|Products|Clothing|<br>& Transport|Manuf.|
|**BY INCOME GROUP**|||||||||
|Developed economies|5.1|6.3|8.1|6.5|8.0|4.2|6.5|6.1|
|Developing economies|7.9|10.1|6.6|14.6|15.8|10.3|17.2|13.2|
|High income Developing economies|6.4|11.5|4.9|12.1|15.5|5.5|14.7|10.6|
|Upper Middle income economies|8.8|11.3|9.6|18.5|16.7|15.5|21.7|16.3|
|Lower Middle income economies|7.1|7.9|8.3|13.5|14.4|10.7|16.8|12.9|
|Low income economies|5.6|4.8|6.6|9.5|9.1|6.5|9.6|11.2|
|Of which: Least Developed countries|5.7|7.4|7.9|9.0|10.0|6.5|11.2|9.3|
|**Memo:**|||||||||
|Developing econ, excl. China & India|7.5|9.7|6.4|12.8|14.8|7.4|15.5|11.2|
|Upper middle income, excl. China|8.1|10.8|9.3|14.1|14.1|8.6|17.5|11.8|
|Lower middle income, excl. India|6.9|6.8|7.7|14.6|13.5|11.5|19.0|13.3|
|**BY REGION**|||||||||
|World|5.9|7.4|7.2|8.1|8.8|6.7|8.3|7.5|
|Developing economies|7.9|10.1|6.6|14.6|15.8|10.3|17.2|13.2|
|Asia|7.5|9.2|8.3|14.7|16.9|10.6|17.2|13.6|
|Latin America & Caribbean|9.7|12.6|11.5|16.6|14.8|9.4|20.6|14.5|
|Europe, Middle East & North Africa|8.9|7.9|5.2|14.1|15.3|8.1|13.9|16.5|
|Sub-Saharan Africa|4.8|8.7|10.0|8.0|8.1|5.2|11.7|6.7|
|**Memo:**|||||||||
|BRICS 5 countries|12.1|14.0|21.6|19.2|18.5|16.6|24.8|16.7|
|OPEC 12 members|10.4|14.3|5.7|16.9|21.1|16.9|14.8|16.6|

Notes: The classification of product groups is defined in SITC revision 2 as agriculture & feed (0+1+2+4-27-28), ores & metals (27+28+68) fuels (3), all manufactures (5+6+7+8-68), chemical products (5), textiles & clothing (26+65+84), machinery & transport (7), and other manufactures (6+8-65-68-84).

Export data in sectoral breakdown is based on 161 countries data available in 2010 from COMTRADE database. Where the aggregate merchandise export data is based on total 192 countries data available in Table 3 from World Development Indicators database. Source: Based on UN COMTRADE Statistics.
# _Selected policy suggestions_

There is a vast literature discussing policy recommendations that nurture the benefits and mitigate the negative effects of migration. Before concluding, we briefly summarize a few selected policy suggestions such as integrating migration and development policies (both in South and North), facilitating the flow on remittances, improving recruitment practices, and facilitating international labor mobility.

Migration should be incorporated both in the development cooperation strategies in the North, as well as in the national poverty reduction strategies in the South (Clemens 2010). Although as of yet rare in practice (UNDP 2009, p. 82), there are some cases of successful strategies for cooperative efforts between the sending and the receiving countries to manage migration (Martin _et al._ 2002).<sup>27</sup> Areas of cooperation include the drivers of migration in the source country, networks that move people across borders, and integrating the legal migrants into their destination countries.

While many developing countries have large stocks of immigrants, very few of them have explicit policies on how to deal with immigration or the capacity to manage their borders effectively. Similarly to the destination countries in the North, there are calls in developing countries to focus on attracting the highly skilled migrants, discourage irregular migration, and reports of growing xenophobia (Lucas 2006). Empowering the sending and receiving countries in the South to manage international mobility will benefit the migrants, the host societies and the country of origin.

Statistics on migration and remittances are often of poor quality, especially in developing countries. Few statistics measure migration flows and data are even scarcer when it comes to transit, circular, or irregular migration (World Bank 2010b, p. 25-26). Official estimates of remittance flows are usually gross underestimates of the true volumes, since a large portion of remittances are sent through unofficial channels.<sup>28</sup>

> 27 For instance, Mali and France initiated annual bilateral consultations at the ministerial level to discuss the integration of Malians who want to remain in France, co-management of migration flows, and cooperative development in emigration areas in Mali. Italy and Albania, on the other hand, have collaborated in a joint police program to combat smuggling and trafficking. (For more examples, see Martin _et al._ 2002) 28 Data collected by the central banks sometime do not distinguish remittances from other low-value transfers such as small-value trade payments. In a recent survey of the central banks (Irving _et al._ 2010)

19
Though we do not exactly match the households to an income increase, decline, or no change, this need not matter for our inequality and poverty estimates. Suppose for example there are two households that live on $2.20 a day, one of these experienced an income drop of 10 cents, and the other experienced no change in income. Even if we assign the wrong household an income drop of 10 cents, the implications on poverty and inequality would not change.

We find that the estimated inequality and poverty from the two approaches are only marginally different. Table 3 reports the poverty estimates in panel A and inequality estimates in panel B. The actual match yields a poverty estimate that is 0.18 percentage point lower and an inequality estimate that is less than 0.01 Gini point (in a scale of 0 - 100) higher. Hence, for Nigeria, we find no evidence of significant differences in measured inequality and poverty between using our predicted matching method and the actual matched households.

Table 3: Comparison of actual and predicted matching for Nigeria

||Number of<br>Households|2019|2020<br>Counterfactual|<br>COVID-19|Net<br>COVID-19<br>impact, pp|Change<br>2019-2020,<br>pp|
|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|
|(A) Poverty rate (%)|||||||
|Actual matching|1,866|30.5|30.5|31.8|1.34|1.37|
|Predicted matching|1,866||30.5|31.7|1.16|1.19|
|(B) Gini index (x100)|||||||
|Actual matching|1,866|34.2|34.3|34.0|-0.23|-0.19|
|Predicted matching|1,866||34.3|34.0|-0.24|-0.20|

_Note_ : _Predicted matching_ estimates poverty and inequality in 2020 using the methodology discussed in Section 3.3. _Actual matching_ maps income changes reported by each household in the phone surveys to the underlying 2019 distribution. Both methods rely on sectoral growth rates to estimate the size of household income shock. _Net COVID-19 impact_ is the difference between the 2020 distribution with COVID-19 and counterfactual distribution.

# **5.2 Random allocation of growth rates**

In our simulation, we assigned the same growth rates within five groups of households. These five groups were identified from the income loss, gain, and no change probabilities in rural and urban areas. Our method requires that the growth rates of these five groups aggregate to the growth in per capita GDP. However, within a group, there are infinitely many combinations of growth rates that would allow us to match growth in per capita GDP. In what follows, we will relax our assumption that within each group all households have the same growth and test how alternative growth realizations impact the poverty and inequality estimates.

27
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1. A four week course for medically trained personnel<sup>on delivery of all</sup> contraceptive methods, management of side effects,<sup>management of maternal</sup> and child health/family planning (MCHtFP) clinics<sup>and population education.</sup> Around 200 trainees are trained at the two centers a year.

2. A three week practical course for IUD Insertion.<sup>This course is offered to</sup> graduates of a Family Planning Clinical Skills course.

3. A four week course for training of trainers.

4. A six week course for Community Based Distributors<sup>(who have no basic</sup> medical training) which covers communication skills,<sup>contraceptive methods</sup> and population education.

Regular in-service courses are also conducted for<sup>all service providers. In addition</sup> the Unit has developed a number of educational<sup>materials and assisted the Ministry of</sup> Health in integrating family planning into the curricula<sup>of the General Nursing and</sup> Midwifery courses as well as setting up family planning<sup>training in all of the eight</sup> provinces.

## f) Evaluation and Research Unit

The Evaluation and Research Unit (ERU) was established<sup>in 1984 with technical</sup> assistance from San Diego State University. The unit<sup>is staffed by four Research</sup> Officers, a Resident Advisor and two support staff.<sup>It has succeeded in developing</sup> data collection system to monitor the council's programmes.<sup>Through the quarterly</sup> reporting system, the Council is kept up to date on stock<sup>levels and service statistics</sup> from all provinces of family planning services.

The Unit has established an inventory of health centers<sup>delivering family planning</sup> services and an inventory model for contraceptive distribution.<sup>As a result, reliable</sup> statistics on CBDs and Clinics performance are available.<sup>Analysis of PP services</sup> provided by institutions other than ZNFPC facilities<sup>using National Health</sup> Information System forms is also done but these data are not<sup>very accurate because</sup> of the irregularity in receiving these returns.

The ERU has also carried out a number of studies and<sup>surveys relevant in developing</sup> strategies for the programme as well as collaborating in<sup>such studies as the 1984</sup> Zimbabwe Reproductive Survey and the 1988 demographic<sup>and Health Survey.</sup> A Family Planning/Population Library has been established<sup>at Spilhaus. Almost a</sup> thousand topics from Zimbabwe and internationally have been<sup>coilected. In addition</sup> the library is connected to POPLINE. The library has<sup>proved to be invaluable to</sup> family planning and research.

# _3.1.2. Ministry of Health and Municipal Clinics_

In 1981 the government of Zimbabwe adopted the primary Health Care<sup>Approach.</sup> Family Planning had to be provided as an integral part<sup>of Maternal and Child Health.</sup> Numerous health care facilities were constructed in rural areas<sup>of the country. There</sup> are now some two thousand health care facilities (hospitals,<sup>clinics and rural health</sup>
- 9 -

everything else constant, freight costs may be higher if cargos are perishable, such as food. The empty cargos are used as a baseline.

18. Finally, the time-specific fixed-effects, _ct_ , are included. The data covers 11 years, from January 2010 to December 2020. About 130 year-month dummy variables are created with January 2010 used as a baseline. The coefficients will reveal the time trend of average freight costs over the sample period.

# **III. Data**

19. The main data comes from the CAREC Corridor Performance Measurement and Monitoring (CPMM) database collected by ADB. The analysis uses data for seven countries in Eastern Europe and Central Asia: Azerbaijan, Georgia, Kazakhstan, Kyrgyzstan, Tajikistan, Turkmenistan and Uzbekistan. The region is of particular interest because many large transport investments have been made to improve road and rail connectivity since the early 2010s. For instance, Kazakhstan implemented a massive infrastructure investment program, Nurly Zhol, to redevelop public infrastructure to connect its main cities, logistics centers and free-trade zones to the border crossings, including ports on the Caspian Sea dry ports. The country spent about US$36.6 billion for five years: 2013-2017 (UNECE, 2019). By the same token, the entire region is expected to be reconnected for more regional trade and integration. Notably, however, there are only a few micro studies that examined transport freight costs at the regional level. Persyn et al. (2022) find that the expected impact of increased road user costs on road freight prices is particularly high in Eastern Europe.

20. Originally, the CPMM database contains over 187,000 observations, covering both road and rail shipments. Excluding data with missing information and apparent outliers,<sup>5</sup> 51,137 road shipment data are used. A total of 8,952 carriers transported cargos among 397 locations. Each route is georeferenced. Note that the database is developed at the stop-by-stop level. Even on the

> 5 To exclude outliers, the letter-value display analysis was used, which identifies extreme values in the tails of the distribution that fall outside of three times the interquartile range (IQR) for each variable.
# Table 3: Daily Casualties

# Panel A: Restrictions on Personal Liberties

||**DUMMY**<br>**VARIABLE**|**EFFICIENT**<br>**INSTRUMENT**|**DUMMY**<br>**VARIABLE**|**EFFICIENT**<br>**INSTRUMENT**|**DUMMY**<br>**VARIABLE**|**EFFICIENT**<br>**INSTRUMENT**|**DUMMY**<br>**VARIABLE**|**EFFICIENT**<br>**INSTRUMENT**|
|---|---|---|---|---|---|---|---|---|
||||**_Addition_**|<br>**_al Controls Om_**<br>|**_itted_**||||
|**STAY ATHOME**<br>|-0.00481<br>(0.00316)|-0.0556***<br>(0.0102)||<br>|||||
|**BANMEETINGS**|||-0.00227<br>(0.00338)|-0.0641***<br>(0.0117)|||||
|**BAN ONPUBLIC**|||||-0.000789<br>|-0.0253**<br>|||
|**EVENTS**|||||(0.00523)|(0.00849)|||
|**RESTRICTIONS ON**|||||||-0.00416<br>|-0.0644***<br>|
|**MOVEMENTS**|||||||(0.00329)|(0.0126)|
|**OBSERVATIONS**<br>|23217|22311|23217|22311|23217|22311|23217|22311|
|**COUNTRIES**<br>|152<br>|152<br>|152<br>|152<br>|152<br>|152<br>|152<br>|152<br>|
|**FIXEDEFFECTS**|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|

## Panel B: Restrictions on Activities

||**DUMMY**<br>**VARIABLE**|**EFFICIENT**<br>**INSTRUMENT**|**DUMMY**<br>**VARIABLE**|**EFFICIENT**<br>**INSTRUMENT**|**DUMMY**<br>**VARIABLE**|**EFFICIENT**<br>**INSTRUMENT**|**DUMMY**<br>**VARIABLE**|**EFFICIENT**<br>**INSTRUMENT**|
|---|---|---|---|---|---|---|---|---|
||||**_Additiona_**|<br>**_l Controls Omi_**|**_tted_**||||
|**CLOSESCHOOLS**|-0.0036<br>(0.00544)|<br>-0.0398**<br>(0.0151)||<br>||<br>||<br>|
|**CLOSE**|||-0.00494<br>|<br>-0.0532***<br>|||||
|**WORKPLACE**||<br>|(0.00427)|<br>(0.0107)||<br>||<br>|
|**CLOSEPUBLIC**|||||-0.00599|-0.0693***|||
|**TRANSPORT**||<br>||<br>|(0.00463)|<br>(0.0108)||<br>|
|**CLOSEBORDERS**||<br>||<br>||<br>|-0.00543<br>(0.00488)|<br>-0.0432<br>(0.037)|
|**OBSERVATIONS**|23217|22311|23217|22311|23217|22311|23217|22311|
|**COUNTRIES**|152|152|152|152|152|152|152|152|
|**FIXEDEFFECTS**|Yes|Yes|Yes|Yes|Yes|Yes|Yes|158|

Note: Standard errors in parentheses *p<0.05; ** p<0.01, *** p<0.001

**In order to show the epidemiological implications of our results we undertake the following counterfactual exercise.** Note first that the parameters for the efficient instrumental variable models are very similar among the different CCMs. The counterfactual exercise uses the estimated model for Lockdowns, but the results do not change when using the other models in Panel A of Table 2. We simulate the evolution of the rate of daily cases and the cumulative number of cases in four scenarios:

- (a) The base case where CCMs are not implemented (a laissez-faire policy, as is the case of Japan or Sweden)

- (b) The case when the CCM is implemented very early on, that is 15 days after the first case is reported.

- (c) The median case where CCMs were implemented around 30 days after the first case was reported.

- (d) The late adoption of CCMs 45 days after the outbreak.

20