rafmacalaba/data-use-mentions-extended huggingface dataset:
given that the task content of jobs may vary with development (LoBello et al., 2019).

Two articles use skills surveys from different countries to test if US-based measures such as O*NET lead to biased results. Saltiel (2020) uses skills surveys from developing countries at varying levels of economic development to identify the share of jobs that can be done at home. He uses an approach similar to DN2020 by assuming that a job cannot be done from home if at least one of several conditions related to the job such as physical intensity, not using a computer, frequent contact with people, and so forth holds. Hatayama, Viollaz, and Winkler (2020) also use skills surveys for an expanded set of 53 countries at varying levels of development. In addition to the type of tasks considered by Saltiel (2020), they incorporate a measure of internet connectivity at home as a determinant of working from home amenability. Both papers find that more educated 5
capital which is comparable across countries and over time allows us to examine the evolution of capital stocks and assists us in gaining insight on the process of agricultural growth. In Figures 2 and 3, we present the frequency distribution of growth rates of the capital components for the 30 countries in our sample.<sup>17</sup> Capital accumulation in agriculture since 1970 has been positive in all of the countries (Figure 2). The median growth rate of agricultural

> 14 Estimated lifetimes are assigned specifically for each tree crop, ranging from 20 years for coffee to 200 years for olives (see Appendix A.2). For a discussion of vintage in the evaluation of orchards, see Akiyama and Trivedi (1987).

> 15 The data set on agricultural capital stocks is available online at: http://www.farmfoundation.org/news/articlefiles/1725-capital_update_2000.pdf.

> 16 Since the dissolution of the Soviet Union, Czechoslovakia, and Yugoslavia in the early 1990s, many of the newly independent countries report data on agricultural gross fixed capital formation.

> 17 Average annual growth rates reported in this paper were obtained from trend regressions (natural log of variable on time).

8
3.5|4.1|4.2|||
|Interest|0.1|0.2|0.2|0.2|0.3|0.4|||
|Total surplus (deficit)b|-0.1|-0.2|-0.1|-0.3|-0.5|-0.2|||
|Primnary balance|0.0|0.0|0.1|0.0|-0.2|0.2|||
|Domestic financing|0.2|0.3|0.1|0.2|0.5|0.2|||

- a. Primary spending is net of interest and transfers to territories.

b. Balance after adjustment of transfers and inclusion of net lending.

_Source:_ Central bank; Clavijo (1998); World Bank and International Monetary Fund staff estimates.

13 Fiscal data from various sources all show the same broad trends described here, although they differ in detail. The central bank's assemblage of data for 1987-95 is the best long-term series.

32
of relative distribution methods. Interested readers are referred to Handcock and Morris (1998, 1999) – but see also Hao and Naiman (2010, ch. 5) – for a more detailed explication and a discussion of the relationship to alternative econometric methods for measuring distributional differences. A method very similar in spirit to the relative distribution one has recently been developed by Silber et al. (2014).

> 7 To keep the notation simple and the graphical displays informative, we will focus throughout the paper on _group-level_ data from underlying continuous distributions. We will also assume that discretization is based on decile ranges with respect to the reference year’s distribution. By extending the fundamental concepts of the relative distribution approach to the grouped data context, Handcock and Morris (1999, ch. 11) allow the analysis of discretized distributions to retain the tractability and interpretability of its continuous counterpart based on ungrouped data. For an application of relative distribution methods to ungrouped samples, see e.g. the Brazilian and Nigerian case studies by, respectively, Clementi and Schettino (2015) and Clementi et al. (2014, 2015).

6
percent and 9 percent in Uganda and Ethiopia, respectively (World Bank , 2016; Hill and Porter, 2017), and by 10 percent across most agroecological zones in sub-

> 13 The global cyclone hazard maps are available from <u>https://doi.org/10.4121/12705164.v4</u> and as global scale files compiled by Russell (2022) from <u>https://doi.org/10.5281/zenodo.7438144.</u> 14 Category-classifications are based on the Saffir-Simpson scale (converted from 1-min to 10-min thresholds).

14
# Level of availability and country level aggregation

The KPIs are available at the level of location or pair of locations (typically a UN/LOCODE). For the official report, the data was further aggregated at country level by taking average, medians and other statistics of the natural unit indicators generated for all locations in the country (or pair of countries for corridor data). Portspecific data is available for just under 500 ports and can be made available for academic purposes upon request. Consignment-specific data cannot be made publicly available due to confidentiality concerns and data sharing agreements with partnering organizations.

For landlocked countries, the observations serving to estimate the dwell time at port of entry are separate from the observations related to shipments going to the country of transit. Some countries classified as landlocked developing countries in our report are not considered to be such in a global, less strict definition. For example, Azerbaijan, a country that formally has access to the Caspian sea and operates its largest port (Baku), was classified as a landlocked country. This data feature stems from the nature of our datasets: all of the maritime-related data solely focused on container shipping. Our indicators are thus relevant to ports and terminals that are able to handle containerships and ignore dry bulk, liquid bulk, LNG/oil, and Ro/Ro terminals. In the future, we plan to include additional KPIs that include the performance of port facilities that handle dry bulk operations.

# Data cleaning and consistency

Tracking datasets are not perfect. Events may be missing or duplicated in the sequence of individual consignments. Sequences may be truncated. Table 3 includes the sequence for a consignment going from Odessa in Ukraine to India in transit to Nepal. In between, the container is transshipped in Istanbul, Salalah, and Colombo. However, the transshipment in Istanbul includes duplications (highlighted in grey). To account for this type of issue, cleaning procedures are implemented prior to slicing the data to produce indicators. Anomalous events are eliminated based on rules that “normal
the thirteen included in the region in 2021 (Table 1).

Closing the gender gap in internet access might not be sufficient to ensure that women benefit from the internet in accessing information and economic opportunities. Nonetheless, evidence suggests that it is powerful. As figure 2 illustrates, countries in which the gender gap is in favor of men (where the gender gap in the internet access index has a high positive value), such as Egypt and Morocco, have low female-to-male labor force participation ratios, while the four countries characterized by equal internet access, indicated by a 0 value for the internet gap index, have the highest female-to-male labor force participation ratio. As figure 3 illustrates, inclusive access to the internet with relevant and secure content is associated with a lower gender gap in labor force participation (The Economist Intelligence Unit, 2021b). Countries with high scores on the Internet Inclusion Index (III), which assesses the enabling environment of adoption and beneficial use of the internet, have greater equality in labor force participation rates.

6
Because loans with MDB participation represent only about 10 percent of total syndicated loan volume, and the financing needs to achieve the SDGs are so large, researchers have examined how MDB participation affects the allocation of funds through syndicates and whether such participation can crowd in additional private funding via syndicates that do not include an MDB, perhaps through demonstration effects. In terms of allocation of funds, in the large dataset described above, syndicates with MDB participation were more likely to lend to borrowers located in countries with high credit and financial risk than others (Gurara, Presbitero, and Sarmiento, 2020). Controlling for other relevant factors that could affect loan terms, those loans tend to carry higher borrowing costs and longer maturities. These patterns are consistent with the notion that MDB participation enables syndicates to finance risky projects that may not have been financed by the private sector alone.

However, rigorously demonstrating that syndicates with MDB participation crowd in additional private credit has been more challenging. The earliest research efforts used countrylevel data to test for a positive relationship between past multilateral lending and current private capital flows, and results were mixed. For example, Rodrik (1995) did not find a significant relationship while Dasgupta and Ratha (2000) and Ratha (2001) did. Because of its granularity, loan-level data has made it easier to isolate the relationships between MDB participation and subsequent private bank lending and the terms of those loans. Even with loan-level data, however, the key empirical challenge is selection bias and the endogeneity of MDB lending (Carter, Van Sijpe, and Calel, 2018). Recent analysis using a comprehensive dataset of syndicated loans very similar to the one described above therefore controls for a large set of fixed effects – at the country, year, country-sector, country-year, and sector-year levels – to absorb all time-varying country- and sector-specific factors that could drive MDB and private 25
# **1 Introduction**

Firm capabilities are key drivers of productivity. Firm capabilities include both managerial and organizational practices, as well as innovation competencies. They include the skills that workers and managers bring to the firm. They are elements of the production process that cannot be bought “off the shelf” (Sutton 2012).

There is widespread evidence from across low, medium and high-income countries that firm capabilities are associated with higher productivity. Better worker skills increase productivity as well as generate productivity spillovers (Syverson 2011, Moretti 2004); firms that are better managed can leverage their technology, capital and workforce more effectively and achieve higher levels of production (Bloom & Van Reenen 2007, Bloom et al. 2014). Bloom, Sadun & Van Reenen (2016) argue that about a third of the observed differences in GDP across countries are related to differences in management practices.

Mozambique has seen two decades of continued positive GDP growth, mostly driven by gains in productivity. However, little of this growth in productivity was due to firms becoming more productive. Decompositions of labor productivity show that most of the gain in aggregate productivity in the last decade was due to on-going structural transformation, the movement of workers and factors of production from agriculture to higher value-added manufacturing and especially to services activities (World Bank 2018). Productivity improvements (also called “within” productivity growth, see Olley & Pakes 1996, Melitz & Polanec 2015) contributed to only a tenth of aggregate productivity growth in the manufacturing sector in Mozambique. In the services sector, the average firm productivity even declined in the period, contributing negatively to overall productivity growth (World Bank 2018).

Mozambique, just like many other developing countries, experiences a high degree of heterogeneity in productivity. The 2018 Enterprise Survey data shows that a formal firm (with five or more employees) in the 75<sup>th</sup> percentile of productivity is between 6 to 12 times more productive – as measured by sales per employee – than a
category is all employees). From this regression we obtain estimates of the earnings differentials between non-professional own-account workers vs. employees, employers and professionals vs. employees, and non-professional own-account workers vs. employers and professionals. In a third set of estimates we separate employees into formal and informal sector employees and estimate the earnings differentials between formal vs. informal sector employees, all self-employed workers vs. informal employees, and all selfemployed workers vs. formal employees. Finally, we re-estimate equation 1 and interact the two self-employed and formal/informal dummy variables to obtain estimates of the earnings differentials between non-professional own-account workers vs. formal sector workers, non-professional own-account workers vs. informal employees, and employers and professionals vs. non-professional own-account workers.

Equation 1 is estimated separately for every county (c) and year (t) for which we have the appropriate variables in the I2D2 data set. This results in estimates of the earnings premium for each country (c) and year (t) combination in the I2D2 data set, ����. ���� is that percent by which the earnings of the self-employed differ from the earnings of wage and salary workers. If EPct is positive, that indicates that there is an earnings premium for selfemployment; if ���� is negative, which indicates an earnings penalty for self-employment.

Tables 3 and Tables 4 summarize the results of these estimations for all countries in our sample by income level and regions. In calculating these means across surveys we weight the estimates in three ways. First, in calculating means across countries, the estimates of the earnings differentials in each country are weighted by total employment for that country and year. In addition, because the number of surveys in the data base for each country are different, the results from each survey (country/year combination) are weighted by the inverse of the number of surveys for each country. Weighted this way, each country is effectively counted once even if there are more than one survey for that country.
accommodate for a change in the survey design vis-à-vis the previous rounds ( i.e., the recall period for certain goods ) .<sup>9</sup> We are aware that the reliability of the district-level estimates of urban poverty is widely debated in light of the relatively small number of sampled households (Hasan et al. 2007; Topalova 2010). Although we use the urban poverty measure at the district level, the results are also robust to using urban poverty at the level of the NSS regions, which are a census-based aggregation of several districts (there are approximately 60 NSS regions in India).<sup>10</sup> To the extent that the limited representativeness at the district level can be considered a classic nonsystematic measurement error in the dependent variable, it should not bias the estimate of the coefficients. It only suggests lower efficiency.

The other district-level data, such as population composition, come from the Indian districts database at the University of Maryland (which has been extracted from the original data in the Indian Census), which we update to 2001 using data from the Indian Census.<sup>11</sup> Data on town populations are available from various rounds of the Indian Census. In addition, for crop production volumes and values, we use the district-level database for India, available from the International Crops Research Institute for the Semi-Arid Tropics from 1980 to 1994 and recently updated to 1998 by Parthasarathy Rao et al. (2004).<sup>12</sup> The district classification was modified during the period of analysis because some districts were split into two units. Topalova (2010) created a consistent classification by aggregating the 2001 districts that originated from the district division of 1987. We conform to this reaggregation and modify the original population and demographic data accordingly.

10
suggested by Natarajan (2019) who proposes four stages of innovation in payments. The first, second and fourth stages proposed by this author are like the types described by the Bank of England (2014). The third, however, differs by introducing the notion of decoupling payment initiation from the maintenance of an account, a process enabled by real-time and transparent data processing between retailers, customers and payment service providers. In table 1, we follow Natarajan’s framework but add an initial level of traditional banking. Of the five stages listed in table 1, our empirical analysis focuses on stages 1-3, since the last two stages are not yet captured in international data.

# **Table 1: Five stages of innovation in the business of payments**

**Stage 1: Traditional banking** . This is the main means of managing money and payments through the 19<sup>th</sup> century centered around bank accounts and paying through cash or checks.

**Stage 2: Introduction of digital access to bank accounts** . This is the digitization of traditional banking with debit and credit cards, and online banking to process cashless payments.

**Stage 3: Prepaid accounts and mobile money** . This includes prepaid payment cards, apps and mobile money accounts which may not require a bank account.

4
12 where xj* and X.* represent world exports of product j and total world exports _exclusive of the intratrade_ of the RTA member countries. Regional trade is excluded in order to more accurately reflect the capacity of Mercosur members to compete evenly in markets where discriminatory trade arrangements do not provide an "unnatural" edge.<sup>8</sup> As such, direct comparisons of the two indices provide an indication of the extent to which Mercosur distorted exports from patterns consistent with comparative advantage. Although they do not measure import diversion directly, they provide closely related information by allowing one to infer whether the additional trade generated by Mercosur was primarily in products in which Mercosur countries had low enough costs to be competitive in third markets. If not, they suggest that the additional trade within Mercosur could have been replaced by more efficient outside suppliers.<sup>9</sup> This study will test the use of these indices in this context using actual trade data.

Before proceeding, it should be noted that there are additional empirical procedures that can provide useful supplementary information concerning changes in the composition and direction of trade that accompanied the formation of Mercosur. Specifically, Table 4 lists the 30 three-digit SITC processed products that met two separate criteria: (i) they recorded at least one-quarter of a million dollars in intratrade in 1988 (this lower limit was set to prevent the tests being biased by marginal products, while 1988 - was selected as a period prior to the implementation - and likely the anticipation of major preferences); and (ii) that registered the highest 1988-94 compound annual growth rates in trade among Mercosur 8RCA indices are generally only computed for processed goods or manufactures because trade in agricultural products is distorted by export incentives and trade barriers which are likely to obscure whether a country has a real comparative advantage, or disadvantage, in these products. The present analysis does not attempt to derive revealed comparative advantage indices for agricultural products and other primary commodities. As such, the present analysis is based on
62,446|
|value|||||||
|Other costs divided by total asset value|_Wm_|41,410|<br>16.1|839.3|0.00|<br>146,091|
|Total revenue (1,000 tenge)|_Y_|41,410|<br>510,205|6,484,125|1.00|<br>410,000,000|
|Transport costs to the nearest city ($/ton)|_CITY_|41,410|<br>1.29|1.91|0.05|<br>14.31|
|Transport costs to the nearest border|_BP_|41,410|<br>35.83|17.73|5.36|<br>80.98|
|point ($/ton)|||||||
|Market Access Index based on transport<br>costs|_MAI_|41,410|<br>0.25|0.34|0.01|<br>1.00|
|Amount of inventories at the end of the|_Q_|41,410|<br>83,400|1,056,314|0.50|<br>104,000,000|
|period (1,000 tenge)|||||||
|<br>Number of firms located in the same|_N_|41,410|<br>2,260|2,719|1|<br>9,317|
|district|||||||
|Number of firms in the same industry<br>that are located in the same district|_M_|41,410|<br>186|281|1|<br>1,891|
|Inventory analysis:|||||||
|Amount of inventories at
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