rafmacalaba/data-use-mentions-extended huggingface dataset:
million. <mark>Many emigrants returned home after a stay of about 10 years,</mark><sup>4</sup> <mark>but the</mark> possibility of getting into an occupation resulting in upward social mobility (mainly self-employment or in the public sector) was associated with a shorter stay in the Gulf states, while the prospect of fewer job opportunities back home post-return significantly increased the length of stay (Czaika and Varela 2015).

Within the GCC countries, the distribution of migrants from Kerala has changed over time, with the Gulf countries becoming more popular over the past two decades. Temporary migration from Kerala to Saudi Arabia remained steady, barely increasing from 510,895 migrants in 1998 to 522,282 migrants in 2013. Migration to other Gulf countries increased substantially. Between 1998 and 2013, migrant flows from Kerala to the UAE more than doubled from 421,959 to 898,962; for Kuwait, it increased from 68,163 to 183,239; and for Qatar, it increased from 62,969 to 106,107. The GCC countries remain a major destination for migrants from Kerala, more so than other parts of India. <mark>According to the National Sample Survey of 2007–08, only 0.9 million migrant workers from Kerala were in other parts of India, compared to more than 2 million Kerala migrants in the GCC countries. Also, while residents of Kerala barely migrated to other Indian states, 2.5 million workers from other parts of India were temporarily working in Kerala during this period.</mark> Previous literature has documented that Indian <mark>emigrants to GCC countries, and their remittances, significantly contributed to the state economy and improved the educational profile of the migrants (R</mark> ajan and Zacharia 2019) <mark>. However,</mark> studies on the transfer of norms and values by return migrants from the Gulf or the Middle East to Kerala, India,
The Polish data consists of the 1993-1996 panel component of the Household Budget Survey conducted by the Polish Central Statistical Office. The survey is fielded throughout the year. We used a balanced panel with 16,552 individuals in 4,919 households with complete demographic, income and expenditure information. More details of this dataset can be found in Okrasa (1999a,b).

Non-random attrition is a potentially serious problem. The University of North Carolina's website and Okrasa (1 999a) analyze attrition in each of the data sets. They find that households with better economic positions and households in urban areas are more likely to drop out of the sample. It is hard to infer whether and how this pattern of attrition affects the results.

The main measures of economic well-being used in this paper are the logarithm of monthly consumption expenditure and the logarithm of monthly income. Both measures are adjusted for household size using an equivalence scale.<sup>3</sup> In the Polish data, the recall period for all expenditure items is one month, while in the Russian data recall periods vary between one week for food expenditure, one month for services and utilities, and three months for clothes, shoes and durables. The expenditure data includes actual expenditure on durables rather than imputed rental values of these goods. Hence, one might worry that finding that most shocks are transitory largely reflects sporadic purchases of durab<sup>1</sup> es or other lumpy goods. To address this concern, the total analysis was repeated using only food expenditures, which accounts for 47% of total expenditure in Russia and 42% of total expenditure in Poland. The dynamics of food expenditures are broadly similar to those for total expenditure, and a discussed in more detail in the sections below.

The analysis below is performed on the whole sample as well as on subsamples conditioned on demographic characteristics. For the measurement of shocks, we take the second period of the 4 period panel as the base period. Hence, the base
, representing the Asian countries, reaching France and Germany.

# _5.2.5 International migration flows through Libyan provinces_

Using data about origin and destination countries registered at the FMPs, we track migration flows connecting migrants’ origin countries to Libyan provinces and Libyan provinces to migrants’ preferred destinations.

Figure 11 presents the network of origin countries and Libyan provinces for 2017 and 2018. Origin country _i_ is connected to Libyan province _j_ if, for at least one round in a given year, the majority of migrants observed at an FMP in province _j_ departed from country _i_ . These graphs show two important changes that occurred between 2017 and 2018; the number of origin countries decreased,<sup>20</sup> and there is a notable decrease in the density of the graph, meaning that migrants from a given origin country are

> 19 For ease of interpretation, we removed connections signaling migrants returning to their home country or intending to remain in Libya.

> 20 The top origin countries disappearing from the graph are Benin, Cameroon, Central African Republic, Democratic Republic of Congo, Guinea, Guinea-Bissau, Kenya, Malawi, Morocco, Togo, and Tunisia. At the same time, we observe the appearance of Pakistan.

14
employment at a large company. In MENA, smaller firms attach greater value to personal references and contacts and therefore “trust” when recruiting, while larger firms attach greater value to credential signaling. Smaller

> 20 The midline survey contains an indicator that is not presented in table 5 on whether respondents were self-employed 12 months and not 7 days prior to the survey. Estimates were statistically significant for women at the 5% level, with a coefficient of 0.03.

21
the free entry condition ( _Vi =0_ ), the formal contract wage _wF (xF, y)_ for a worker of type _y_ producing at _xF_ is following.

The wage in sector _i_ is the weighted average of the worker’s productivity and the worker’s outside options. Evidently, regulations affect the formal wage bargaining process.

# **III. DATA AND CALIBRATION**

The model is calibrated using data from the 2015 Moroccan Labor Force Survey (MLFS), the latest publicly available to conduct this research. The survey have been conducted by the High Planning Commissariat of Morocco, covering all residents on the national territory. The MLFS is representative at the strata (urban/rural) and region levels. The MLFS provides information on wages and has large size (60,000 households of which 40,000 are urban, and about approximately 270,000 individuals every year).

Given the nature of the model, our sample excludes agricultural employment and the public sector. The agricultural sector (albeit large) is mainly composed of unpaid family workers, a group that is unlikely to be affected by changes in the labor policies assessed in the study. Also, the main features of public sector employment in Morocco (wages, size, hiring/firing decisions) are not necessarily driven by the model’s assumptions, despite the fact that this sector is quite compliant with labor regulation. Indeed, public sector decisions to set wages, hire and fire workers, etc., are not likely to follow the assumptions of the value functions proposed by the model but to be driven by other factors (e.g. politics / pork barrel / social peace). As a result, our sample choice is largely urban (75%) and accounts for 51% the national labor force (Table 1). While this sample choice leaves us with about half of the overall Moroccan labor market, we believe that it is a good representation of the share of the market that is somehow affected
12.6 percent if road development is complemented by port improvement projects.<sup>37</sup> At the country level, the results exhibit significant heterogeneity. When border delays are high, road network development primarily boosts internal connectivity. However, when combined with additional reductions in border and port delays, intra-continental and intercontinental connectivity significantly improves, generating substantial benefits. Major increases in trade flows are observed in Burkina Faso, Burundi, Cameroon, Cˆote d’Ivoire, the Arab Republic of Egypt, Kenya, Malawi, Senegal, Uganda, Zambia, and Zimbabwe. Uganda, Zambia, and Zimbabwe, being landlocked countries, indirectly benefit from port investments in transit countries such as Tanzania or Mozambique.

Differences in outcomes without and with maritime preferences stem from variations in the assumptions used to construct the transport network and its counterfactuals. For instance, Egypt experiences a significant decrease in transport costs when a maritime preference is assumed. In this case, flows transition from longer maritime routes in the baseline scenario to shorter land routes when land border delays are substantially reduced between Egypt and its neighbors. This explains why the average impacts are considerably larger with the maritime preference than without.

GDP effects arise from the interplay of complex general equilibrium effects, relying on a country’s characteristics and position in the global trade network. The countries that gain the most include Botswana, Cameroon, Namibia, Niger, and Zambia. Egypt and Tunisia also observe substantial GDP gains when assuming a mar-

> 35In line with previous studies, customs delays have been shown to be a significant barrier to trade. For example, Volpe Martincus, Carballo & Graziano (2015) find that if all exports in Uruguay had been subject to physical inspection, and if such inspections had lasted two days, then total exports in 2011 would have been 16.4 percent lower than they actually were. Using transaction-level data from El Salvador, Carballo, Graziano, Schaur & Martincus (2022) show that upgrading transit systems to streamline border processing in developing countries
and Zimbabwe), where drought is a major constraining factor. We therefore use it in interaction with dummy variables for those two zones ( _Sahelian_ and _South_ ). The square of _Rainfall_ is also included for the Sahelian countries where we expect a decreasing marginal effect of precipitations (Blanc _et al._ , 2008).<sup>15</sup> In countries with a Guinean climate (Benin, Cameroon, Chad, Côte d‟Ivoire, Nigeria, Senegal and Togo) and in the eastern zone of

> 14 The shares of cropland dedicated to cotton cultivation are taken from Monfreda et al. (2008). They are based on the potential for cotton cultivation for the year 2000. See Figure 3 for the 5 minutes (~10km) resolution map of the densities used and the annual cumulative rainfall. The major limitation associated to the use of this dataset is the fact that it rests on a static estimation of land use as it is only available for 2000. However the potential for cultivating cotton (estimated with satellite data and agricultural inventories) is little submitted to time variations. This should there therefore affect our estimation only marginally.

> 15 In the sub-tropical countries of Eastern Africa, the effect of cumulative precipitations is much more limited. Identifying the potential non linearity of this effect is thus not possible.

12
agency). However, the coefficient on having a Ministry regulator is significant at the 5% level and the wrong sign (ie a Ministry regulator is _positively_ associated with higher private investment and an _independent_ regulator negatively associated.) Pargal puts forward some ingenious arguments why this might be so but we do not find them convincing and they are, in any event, inconsistent with the funding result.

Pargal’s paper takes the estimation of the impact of regulation on outcomes in the power sector rather further forward but this paper still raises at least as many questions as it resolves. That seems to arise primarily because of the problem of having to rely exclusively on data drawn from the characteristics of reform legislation rather than from subsequent observed practice.

The main lesson to be drawn from the analysis seems to be that only when data which include regulatory <u>experience</u> and the amount of <u>actual</u> competition will it be seriously possible to try to disentangle the effects of regulation – and individual regulatory governance characteristics - in a regression analysis approach.

One other feature is that the availability of comparable cross-country data on _investment_ in the electricity industry in DTEs seems to be confined to data on _generation capacity_ . This seems to be the case even in the Calderon-Serven data set. This is most unfortunate given the emphasis (a) on transmission capacity for successful unbundling and (b) on the role of distribution for electricity reform in DTEs.

# **5.3 Empirical Studies of the Impact of Regulatory Agencies on Telecommunications Outcomes in DTEs**

There is a rather larger literature on the impact of regulation on telecom outcomes than on electricity outcomes. This is, not least, because there are more countries with independent regulatory agencies which have 5 years or more of existence (at least of legal existence)<sup>49</sup> . However, because this paper is primarily concerned with electricity outcomes, we will concentrate on three of the most recent and relevant papers – Fink
and on ports and ship datasets from open-source datasets and a database made available through the International Maritime Organization (IMO). Fields available in the port calls dataset include:

- Type of the event: arrival or departure

- UN/LOCODE 7 Port Name

- <mark>Date / Time in UTC</mark>

- Vessel particulars:

- <mark>IMO number</mark>

- <mark>MMSI (Maritime Mobile service Identity) number</mark>

- <mark>Vessel name</mark>

- <mark>Vessel type</mark>

- <mark>Draught at the time of the recording</mark>

- Length, width, and max draught

The dataset was prepared using MarineTraffic port call data covering over 5,000 container ships calling at over 800 ports during the first two quarters of 2022. Based on estimated time differences between recorded arrivals and departures to port facilities, an indicator of turnaround time per port was constructed. In addition, ship data, including capacity in twenty-foot equivalent units (TEU) is available. Ship types range from small feeders with capacity of up to 1,000 TEU to ultra large container vessels with capacity starting at 14,501 TEU. Additional details regarding this data source are in Appendix 4.

# **3. Definition and processing of KPIs**

From tracking data to country indicators The micro-logistics datasets used for the LPI 2023 consist of large numbers of voyages of individual shipments (e.g., container or parcels). For shipping data, they contain the port call sequence. The raw tracking data consists of the following fields:

- ID of the voyage

- Origin, destination

- Coded location of the event: tracking data localize the event by port, airport, or more generally UN/LOCODE.

- Time of the event

- Type of the event (structured categories, specific to the dataset): e.g., container loaded on ship, parcels arriving at the bureau of destination, port
7 affected.8 In a related vein, noting the inability of most developing countries to borrow in their own currencies and thus the limited ability to hedge foreign exchange risk, Femandez-Arias and Hausmann (1999) have argued that, with floating exchange rates, countries will reduce unhedged foreign currency borrowing by simply reducing the total amount of foreign currency borrowing, thereby experiencing less investment and growth.

Addressing the need to attract foreign direct investment (FDI) as a stable source of longterm capital, Benassy-Quere, Fontagne and Lahreche-Revil (1999) analyzed the locational decisions of a risk averse multinational firm, considering two foreign locations in order to reexport (hence, trade and FDI are assumed to be complements). In this model (in which two developing countries are competing for FDI inflows), it is shown that exchange rate volatility is detrimental to FDI because it increases the variance of local costs (hence profits measured in the investing country's currency), thus diverting FDI to the other country with a smaller variance; this prediction is empirically confirmed by a panel of 42 developing countries receiving FDI from 17 OECD countries during 1984-96. In particular, while the real depreciation of a developing country currency against that of the investing country is shown to increase FDI inflows (through what they call the competitiveness effect), an increase in nominal exchange rate volatility reduces FDI inflows, controlled for distance and other country characteristics. As a policy implication, the importance of stabilizing the exchange rate against the currency of one's most important FDI supplier needs to be underlined.<sup>9</sup> Finally, regarding the effect of exchange rate volatility on inflation and economic growth, Ghosh, Gulde, Ostry and Wolf (1997) investigated a data set covering 140 countries over 196090 by considering the degrees of exchange rate flexibility measured effectively in terms of officially stated classification. For both the entire sample and for the sample of developing countries,<sup>1</sup> I they find unambiguously that inflation is both lower and
# **Appendix 1: Teacher earnings and student performance**

We use two approaches to explore the association between teacher earnings and student performance—first a cross-country approach building on our estimates, second a within-country approach using PASEC microdata from 10 African countries.<sup>37</sup>

# _Cross-country_

We carry out exploratory cross-country correlational analyses between teacher earnings differentials and other observed variables. Specifically, we examine the association with student achievement using two measures. The first measure of student achievement is the harmonized test scores from the Human Capital Index database, recently developed by the World Bank (2020).<sup>38</sup> This database harmonizes results from international and regional testing programs to make student achievement comparable across nearly 160 countries and economies in the world, and it covers all countries in our sample. The harmonized test scores range from 300 to 600 points and are expressed in the unit of the Trends in International Mathematics and Science Study (TIMSS) testing program. The average score across countries is 431 points, with a standard deviation of 69 points.

Second, we use the rate of “learning poverty” defined as the share of children who are unable to read and understand a simple text by age 10 (World Bank, 2019a). The learning poverty indicator takes into account both the minimum reading proficiency of children in school as well as the proportion of children who are out of school. Compared to the Human Capital Index measure, the learning poverty measure captures a fuller view of student achievement (with its accounting for out-of-school children), but its data coverage is less comprehensive. Among the countries in our sample, only six have learning poverty data.

> 37 The mean and standard deviation of test scores for each grade level are reported in Table A4.

> 38 There are two regional testing programs in Sub-Saharan Africa: the Southern and Eastern Africa Consortium for Monitoring Educational Quality (SACMEQ) organizes the literacy and numeracy assessments of Grade 6 students in most Anglophone Sub-Saharan African countries;
region only in the presence of non-humanitarian, non-neutral interventions (Abu-Bader and Ianchovichina, 2018).

While in the MENA region there are many unrefuted studies on the limited linkages between relative welfare distribution measures and perception (Verme et al., 2014; Ianchovichina et al., 2015), the link between perception and absolute measures has been less explored. As opposed to relative measures, perception and absolute measures unambiguously indicate an increase in many MENA countries; this is reflected in the close to 20 percent increase in the absolute Gini and absolute Foster and Wolfson indices.<sup>2</sup> When focusing on absolute polarization and applying the relative distribution method,<sup>3</sup> we observe two contemporaneous effects: (i) a general hollowing out of the center of the distribution (see, _inter alia,_ Alderson and Doran 2011, 2013); and (ii) a significant concentration in the lower tail, representing the so-called _downgrading effect_ . The question that arises in this context is whether this growing absolute polarization, besides showing a similar trend, is also related to the generalized sense of dissatisfaction observed in many Arab countries.

Our hypothesis is that absolute polarization is indeed highly correlated with the perception of households of their socioeconomic status. The worse perception of poor, vulnerable, lower-middle class households, as compared to middle-and-upper-class households mirrors the ongoing process of the hollowing out of the welfare distribution and its concentration in the tails, in other words it mirrors polarization.

Using consumption and socioeconomic perception data from the three comparable rounds of household surveys, we test whether polarization is perceived by households and seek to establish if it contributes to the general malaise and sense of instability reported by Moroccan households. Moreover, as polarization is a synonym of the hollowing out of the central deciles and growing gap between the poorest and the richest, we expect that the lower and central deciles will, as the years go by, feel relatively more threatened by economic downturns and by the fear of losing ground _vis-à-vis_
- 24 -

products' labor intensity ratio differ from the United States average. The table shows the number and per cent of labor intensive products falling in each broad industry group and also classifies these items in a range of "very high" to "moderately" labor intensive. 17/ Among the 19 industry groups listed, the highest concentration of labor intensive products is now in textile mill products (25 products or 13 per cent of all four-digit SIC items) and apparel and textiles (31 products or 17 per cent of all labor intensive items). Furthermore, the table shows that these products are entirely coacentrated in the "very high" labor intensity group (cotton mills, artificial fiber mills, men's suits and children's outerwear), or are distributed between the "very high" and "high" groups. Appendix Table 1 shows that these products (identified in terms of their corresponding SITC group) were also among the most highly labor intensive goods in the mid-1960s.

Aside from textiles and clothing, wood products (SIC 24) have the third highest concentration of labor intensive goods (17 items or 9 per cent of the total) while 7 per cent of the labor intensive products fall in SIC 34 (fabricated metals) and SIC 39 (miscellaneous manufactures). Analysis of the underlying product data (see Appendix Table 6) show various furniture products

- 17/ In Table 6, a four-digit SIC product is classified labor intensive if its factor proportions index is 10 points or more below the United States average. This "cut-off" point is lower than that used by the NBER due to the mixed export results developing countries had for products closer to the United States average. Furthermore, products are considered to be "very highly" labor intensive if their ratios are 40 points or more below the U.S. average, while items with a "high" labor intensity are those with ratios between 0.60 and 0.74. "Moderate" labor intensive products are those