rafmacalaba/data-use-mentions-extended huggingface dataset:
6. Frequency with which shipments reach the consignee within the scheduled or expected delivery time <mark>Respondents rate up to eight countries along those six dimensions. The countries are selected according to the country of operation of the respondent. The country selection engine combines randomness and trade patterns. The LPI tells how easy (perceived) it is to establish efficient and reliable connections between trading countries. Conceptually, the LPI is a revealed supply chain accessibility metric. The LPI expanded on the concept of logistic friendliness introduced by Daley and Murphy [1999]. These authors were measuring through surveys the attractiveness of cities in the United States, as regional hubs of logistics activities.</mark> <mark>The choice of a survey-based instrument was in part dictated by necessity. When the LPI was created, "hard" supply chain data was scarce, and hardly comparable across economies. Initially, the LPI survey covered lead time to trade or in customs for different modes, with focus on the respondent’s country of operation. Yet the dispersion of results for a given country was higher than the perception-based LPI, and global surveys were found inadequate to measure lead time or delays. The LPI survey was discontinued after the 2023 edition to focus on more general attributes of logistics.</mark> <mark>The Logistics Performance Index (LPI) is the primary supply chain benchmark for policymakers and has motivated reforms since 2007. The LPI has been used as a component indicator in several high-level dashboards including the Sustainable Development Goals or those of the World Economic Forum.</mark><sup>5</sup>

# <mark>Moving towards Big Data</mark>

<mark>The perception-based approach had shortcomings. L</mark> PI scores reflect a quantification of qualitative perceptions on an ordinal scale and are thus subject to noise. Sampling is nonrandom since respondents choose whether to participate, possibly leading to selection bias. This creates difficulties when comparing small changes between countries and within countries over time
where the subindex _m_ corresponds to one of four different transportation modes: car, metro or metrobus, bus, and walking; _λnism|ns_ is the share of workers that commute to location _i_ from location _n_ working in sector _s_ using the transportation mode _m_ ; time _nim_ is the average commuting time across municipalities _n, i_ using _m_ ; _γnsm_ are origin-transportation-sector fixed effects; _γism_ are destination-transportation-sector fixed effects, and _εnism_ captures the measurement error observed in the data of this gravity equation.

The goal is to recover the parameters _θs_ after knowing _βs_ and _δd_ described in the previous section. The parameter _θs_ captures how sensitive workers are to commute in the formal/informal sector. From the evidence in Section 3, the expected result is that _θI > θF_ , suggesting that informal jobs are easier to subsitute across location. I estimate this equation via the Poisson regression by pseudo maximum likelihood (PPML) to include the zero commuting flows between municipalities. Given the set of fixed effects, the identification comes from comparing the workplace decision of workers that use the same transportation mode and live (work) in the same municipality and sector, but work (live) in different places. Panel A in Table 4 reports the results. As expected, there is a negative relationship between commuting flows and the average commuting times. I find that the commuting elasticity in the formal sector is 3.11, and in the informal sector it is approximately 4.66. These values are consistent with the theoretical assumptions, and they confirm that informal workers are more sensitive to commuting costs than formal workers.

**Trade Elasticities:** To estimate the trade elasticities, I use the 2017 OD Survey focusing on data on trips to different establishments. I restrict the sample to trips to restaurants, retail shops, and factory-outlets. I assume that people move across the city and spend their income on different consumption goods. To estimate a different trade elasticity for the informal and formal sectors, I use the fact that most informal establishments in Mexico
example, a firm with a shock equal to 1 (i.e., its destinations’ GDP declined on average by 1%) is more adversely affected by the GFC than a firm with a shock equal to -2 (i.e., its destinations’ GDP grew on average by 2%).

From the point of view of a firm or a worker in Brazil, a country that did not contribute

> 16To further probe the worker database quality we estimate Mincer regressions for monthly real wages including a polynomial on worker age following Fr ́ıas et al. (2022), worker tenure at the firm, 4-digit industry and year fixed effects and either dummies for males and for higher education or worker fixed effects. The estimates show expected patterns: e.g., a male wage premium of 26% and a higher education premium of 85% (see Appendix Table A2).

> 17Brazil’s customs agency is not authorized to share its customs data, but counterparts in that agency constructed our GFC firm shocks using code we provided and we merged those to our worker and firm panel databases based on unique firm identifiers.

> 18The 2007-2008 real GDP growth rates are taken from the World Development Indicators.

10
effective policy packages should support earnings and household income._ Responses taken in European OECD countries -- such as partial unemployment insurance, expanding cash transfers to poor workers, and temporary wage subsidies–may be priority interventions in those countries where hours and earnings adjustments dominated.

25 Surprisingly little is known about how well passive and active labor market program work during severe downturns, despite the large literature on the impact of active and passive labor market programs on labor market outcomes (Betcherman et al 2004; Fretwell et al 1999; Auer et al 2005), 26 For more detail see Paci et al 2011 27 This is according to a survey of policy responses to the crisis in 52 developing countries conducted by the World Bank and the International Labor Organization. See Robalino, Newhouse, and Rother (forthcoming) for details.

> 28 Argentina and Mexico concentrate on employment generation while Brazil had a large unemployment insurance scheme (Marquez 2000). Costa Rica and Argentina also had wage subsidy programs.  Argentina’s _Jefes_ program promoted employment in Argentina’s private sector, to recover lost jobs in companies with sufficient capacity (Marshall 2004). In Mexico, though there were not many legal reforms of labor market policies, changes took place largely through collective bargaining.  Social security reform was implemented in 1995, but did not involve changes in employer level contributions (Marshall 2004). In Korea, job creation and a Wage Claim Guarantee was the thrust of the policy response to the East Asian Crisis (Phang and Kim, 2001). They also had a job training program to encourage reemployment (Hur 2001). An Employment Insurance Scheme that was active from 1995 was extended to cover all enterprises in 2008 (Phang and Kim, 2001). Even in Thailand the emphasis of labor market policies in response to the crisis was direct job creation rather than self-employment. Informal workers were not protected, but likely to benefit from ―income support activities‖ such as tax reductions on VAT, price supports for rice, etc. (Betcherman
- 70 -

is 3,312,449 CFAF. If such schools were filled to capacity, the unit cost would drop to just below 2 million CFAF (at current total expenditure levels).

Second, the costs of buildings and equipment are exceptionally high at the post-secondary level. Amortization of these adds 65% to unit costs. This is a result of the transfer undertaken since 1980 of many post-secondary VTE institutions to Yamoussoukro, the C8te d'Ivoire's new capital. This required the construction of an entire new campus for INSET. This transfer process continues and will no doubt keep post-secondary VTE costs at very high levels.

# 7.4 Internal Rate of Return

The private and institutional cost data discussed in section 7.3 are the building blocks to calculate the cost streams of VTE. Table 24 shows the private and social costs per student per year of VTE.

The social costs are four to five times higher than the private costs in the cases of secondary VTE and teacher training. The high institutional costs of post-secondary VTE make that the social costs are almost seven times the private costs. Since there are no institutions associated with apprenticeships, social costs equal private costs. The duration of the cost stream was calculated as a weighted average (weighted
we excluded all points within 10m of that point (to avoid selecting multiple points too close together). Enumerators visited each of these points, and identified when a point fell on cultivable land.<sup>17</sup> With the help of a key informant (often the village leader), they then recorded the name of the cultivator of the plot, their contact information, as well as a sufficiently detailed description of the plot. These listed cultivators form our main household survey sample. For each cultivator, one of their identified plots was randomly selected, which we refer to as the _sample plot_ .

# **2.2.2 Survey**

Our baseline survey covered 1,695 spatially sampled cultivators in August - October 2015. The survey includes detailed agricultural production data (season-by-season) for seasons 2014 Dry through 2015 Rainy 2 (June 2014 - May 2015). The dates of this survey and follow up surveys, along with the agricultural seasons they cover, are presented in Figure 1. Details of the construction of key variables we use for the analysis are presented in Appendix A. As mentioned above, this is not a “true” baseline as some farmers had already gained access to irrigation in 2014 Dry. However, relatively small parts of the site had access to irrigation at this point; in Section 3.2.1 we highlight that 2014 Dry adoption of irrigation is less than 25% of adoption in subsequent dry seasons, and in Section 3.1.1 we show balance across the command area boundary in household and plot characteristics. A panel of plot-level production and input data are maintained for two plots, which were mapped using GPS devices for precise location and area measurement. The two plots on which panel data is collected represent the primary data for analysis; they include the sample plot (described above) and the farmer’s next most important plot (defined at baseline; we refer to this as the “most important plot” or “MIP”
and the p-values of F-tests for equality of parameters between the welfare and power models (Column 14).

4.4. Welfare This section will dicuss the estimation results for welfare and power in 2001 of Model 2. It discusses first the basic characteristics, and then moves on to economic and social features.

30 Defining the reference group at the hamlet rather than village level leads to smaller (closer to zero) and less significant coefficients. In contrast to ?, there is no evidence that inequality matters for self-assessed well-being. Quintile indicators, which account for the households position within the consumption distribution either for the population as a whole or specific to the individual’s language group, are insignificant. 3! The 6th to 9th rungs of the power distribution are merged into one single rung for the comparison with the welfare distribution in rows 17 and 18.
.2 and 4.2 employ variables in TA 3 standardized by those in NPS 2019/20. Food and non-food expenditures, household size, and age in both surveys are transformed to normality using the Box-Cox method before standardization. The imputed poverty rates for TA 3 use the estimated parameters based on the 2019/20 data. 1000 simulations are implemented. The true poverty rate for TA 1 is the estimate directly obtained using the consumption data in TA1 and the poverty line in NPS 2019/20 deflated to 2022 prices with the annual WB deflator. The estimates shown in bold or with an “a” respectively fall within the 95% CI or one standard error of the true poverty rate. The underlying regression results are provided in Appendix A, Table A.5.

54
# Figure 16: SAR: Investment needs

# A. Quality of infrastructure

# B. Infrastructure investment needs

<!-- Start of picture text --> Index, 1-7, 7 = best Percent of GDP<br>7 8<br>Advanced economies EMDEs SAR<br>6 7<br>6<br>5<br>5<br>4<br>4<br>3<br>3<br>2<br>2<br>1 1<br>0 0<br>Transport Electricity Water and Flood Irrigation<br>sanitation protection<br>C. Public health expenditure D. Human capital indicators<br>Percent of GDP Index, 0-1, 1 = best Number of years<br>3 EMDEs SAR Human Capital Index<br>0.8 Learning-adjusted years of school 12<br>(RHS)<br>2<br>0.7 9<br>0.6 6<br>1<br>0.5 3<br>0.4 0<br>0 SAR EMDEs Advanced<br>economies<br>E. Agriculture output F. Agriculture research spending<br>Percent of total output Percent of agriculture GDP<br>30 0.7 Range Median<br>0.6<br>25<br>0.5<br>20<br>0.4<br>15 0.3<br>10 0.2<br>0.1<br>5<br>0.0<br>0<br>1990 2021<br>2000 2002 2004 2006 2008 2010 2012 2014 2016 2018<br>2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017<br>2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016<br><!-- End of picture text --> Sources: Agricultural Science and Technology
WPS3450

# **Discovery and Development: An Empirical Exploration of ‘New’ Products***

Bailey Klinger Daniel Lederman Office of the Chief Economist Latin America and the Caribbean World Bank

**Abstract** : _This paper uses disaggregated export data to explore the relationship between economic discovery and economic development. We find that discoveries, or episodes when countries begin exporting a new product, are not limited to so-called ’dynamic’ industries, rather they also occur in traditional sectors such as agriculture. In addition, the data suggest discovery is a component of the stages of  productive diversification that occur with development, following a consistent pattern: discovery activity peaks at the lower-middle income level and then declines. Based on this pattern, we show that discovery in the 1990s occurred with a higher than expected frequency in Eastern Europe and Central Asia, and lower than expected frequency in Sub-Saharan Africa. Discovery is not found to be a product of structural transformation based on changing factor endowments across income levels. Beyond export growth, population, and development, there are no significant and positive relationships between the expected drivers of entrepreneurship and the frequency of discovery. Combined with the finding that higher absorptive capacity and lower barriers to entry are associated with a reduction in discovery, this suggests that market failures arising from imitation and free-riding may be inhibiting the emergence of new export products in developing countries._

## World Bank Policy Research Working Paper 3450, November 2004

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the view of the World Bank, its Executive Directors, or the countries they represent. Policy Research Working Papers are available online at http://
5.3|0.1|34.0|6.5|
||<br>Lower middle income|39|8.8|8.0|4.7|2.4|24.8|6.0|
||Low income|19|13.7|10.2|10.1|3.3|41.5|13.0|
|Byregion|||||||||
||High income: OECD|14|2.6|2.7|1.2|0.2|4.7|1.9|
||High income: non-OECD|13|5.1|4.9|1.9|1.8|8.1|5.1|
||East Asia & Pacific|17|a3|5.5|4.7|2.4|20.2|3.6|
||Europe & Central Asia|17|tad|6.2|5.2|0.4|20.8|6.7|
||LatinAmerica&Caribbean|27|9.6|7.2|6.8|4.1|34.0|16.9|
||<br>Middle East & North Africa|10|4.6|4.9|2.6|0.1|9.5|46|
||South Asia|5|5.9|5.9|0.5|5.2|6.4|6.0|
||Sub-SaharanAfrica|26|137|8.8|8.9|3.3|41.5|12.8|

Source: Global Financial Development Database, 2008-10 data.

Note: OECD = Organisation for Economic Co-operation and Development. a. Weighted average by total population.
# **5.2 The Bilateral Overall Trade Restrictiveness Index**

To show how heterogeneous elasticities may affect trade impact calculations, we build on the multilateral Overall Trade Restrictiveness Index (OTRI) framework, developed by Kee, Nicita and Olarreaga (2009), based on the Mercantilist Trade Restrictiveness Index of Anderson and Neary (2003). For any importing country, with different trade policies on different products, the OTRI is the uniform tariff equivalent that would generate imports at the observed levels. Here, we define the OTRI as the uniform ad valorem tariff of country _c_ (the EU) with respect to imports from country _d_ (the UK), facing bilateral tariff _τcd._ In Section A3 of the Appendix, we derive and decompose the bilateral-OTRI into the following two terms: where _τ_ ̄ _cd_ and _ρcd_ are the average tariff and the covariance of tariffs and the negative trade elasticity, respectively.<sup>28</sup> From Equation (18), it is evident that the OTRI is higher when the bilateral trade policy of _c_ on products from _d_ has a higher average or a high correlation with the bilateral trade elasticity. Unlike the TRI, the OTRI is not affected by the variance of tariffs.

It is straightforward to write the trade impact of trade policy in terms of the decomposition of the OTRI:<sup>29</sup> Thus, the higher is the average tariff, or the covariance between tariffs and trade elasticity,

> 28Please refer to Section A3 of the Appendix for the definitions of these variables.

> 29See Section A3 of the Appendix.

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the ORBIS interface, we delete all firms flagged as inactive because they correspond to firms that were dissolved, liquidated or under bankruptcy.

- _Complement data gaps when information as of 2019 was not available:_ When there was no information available in terms of sector of operation, revenues, employment, or profits as of 2019, we employed data reported as of 2018 or 2017 as the best proxy. The financial information was deflated to report prices as of 2019 using the WBG GDP deflators. All financial indicators are reported in thousand USD.<sup>41</sup>

- _Review financial data referred to as unconsolidated information to avoid double counting (or overestimation of the state footprint):_ Although ORBIS provided a variable to indicate whether a company reports as a consolidated unit (as parent with their subsidiaries) or unconsolidated (as individual), we did several cross-checks to ensure firms with information were reporting in unconsolidated terms.<sup>42</sup>

- _Review ownership information:_ Cross-check with existing information to confirm the level of state participation as of 2019. When official sources or complementary information suggested firms do

> 41 When data was provided in local currency, the exchange rate as of December of 2019 by the Central Bank was employed to convert the information to USD.

> 42 When the company only reported consolidated information, it was used for the aggregate dashboard, but to avoid double counting issues, the information of the subsidiaries is not included. Those firms are flagged as n.a., to indicate their information is captured by the parent company in the database.

26
Argentina’s trade policy in the past decades has swung from episodes of open trade to episodes of strong protectionism. Periods of protectionism were marked by an anti-export bias and import substitution policies, which have their roots in distribution conflicts that favored industry over agriculture in a country with a fundamental comparative advantage in agriculture (Brambilla, Galiani and Porto 2018 and Galiani and Somaini 2018). The anti-export bias hampered the productivity growth of the agriculture sector, while the import substitution strategy was unsuccessful in promoting industrialization. As a result, Argentina never developed its trade potential.

This paper examines the characteristics and dynamics of exporters in Argentina between 2007 and 2017, during most of which the country adhered to a protectionist policy stance. Employing highly disaggregated firm-level trade information, we benchmark firm-level export performance in Argentina during the period of 2007 to 2017 with respect to similar countries using the World Bank Exporter Dynamics Database, a catalog of comparable indicators for 70 developing economies (Fernandes, Freund, and Pierola 2016). We also investigate the role of imported inputs in the export performance of firms in Argentina during this period. We estimate premiums for exporting firms that are also direct importers of intermediate and capital goods relative to those that are not direct importers for a wide range of firm-level export outcomes (export value, export growth, number of destinations, unit values) controlling for unobserved firm heterogeneity and temporal cyclical effects. We conduct the analysis for the overall export sector and for individual economic sectors. Our work is complementary to Albornoz, Lembergman, and Juarez (2018), who study the relationship between the real exchange rates and firm-level performance of Argentina’s exports between 2003 and 2011, a period of high export growth.

The contribution of our work is threefold. First, to the best of our knowledge, we provide for the first time a systematic benchmarking of firm-level export dynamics in Argentina. Second, we document the important role of intermediate imported inputs in determining export outcomes in Argentina. Third, we contribute to the public good of the
Table 5: Psychosocial impact of days since last worked

||(1)<br>Wellbeing|(2)<br>Stress|(3)<br>Sociability|
|---|---|---|---|
|Days Since Work|-0.098|-0.064|0.042|
||(0.013)|(0.012)|(0.019)|
|Shrp. q-val: Days Since Work|0.001|0.001|0.009|
|Observations|3148|3165|3165|

**Notes:** This table reports the impact of an additional day since a respondent last worked on the three margins of psychosocial well-being which were collected in the weekly surveys. Each column shows the OLS estimates with individual, camp and enumerator fixed effects, a weekly time trend, and sociodemographic controls as determined through a double-selection LASSO procedure (Belloni, Chernozhukov, and Hansen, 2014). The sample includes only those in the employment treatment arm. Standard errors are clustered at the block level.

Table 6: Employment impacts on purposefulness

||(1)<br>Value to family|(2)<br>Value to community|(3)<br>Value to community|
|---|---|---|---|
|Work|0.112|0.072|0.006|
||(0.072)|(0.090)|(0.081)|
|Cash|-0.063|-0.086|-0.067|
||(0.079)|(0.110)|(0.100)|
|Shrp. q-val: Work|0.543|0.752|1.000|
|Test Work=Cash|0.006|0.068|0.345|
(2001) highlight the need for additional river discharge data (especially since the mid-1980s).

# **River basin characteristics**

The two rivers included in this analysis vary in basin characteristics. The Mekong and Zambezi represent a magnitude of river lengths (4350 km and 2,574 km) and basin areas (788,000 km<sup>2</sup> and 1,390,000 km<sup>2</sup> , respectively). These basins are international and have a critical role in the socio-economics of the riparian states including: food, water, transportation, electricity, natural resources, and cultural identity. Both basins are governed by treaties (TFDD 2008). Table 2 summarizes and compares river basin statistics including: geographic and socio-economic calculated from the watershed delineation in ESRI ArcGIS, which we suggest is due to the level of precision in the coordinates compared to the HydroSHEDS flow accumulation grid.

13