rafmacalaba/datause-encoder-data huggingface dataset:
as web design, software programming, or network management). The surveys also include items to determine the use of computer skills in daily life, which could be a broader measure of the availability of such skills in the workforce—although it is also plausible that such skills may not be required in their jobs.

The STEP surveys also gather information on noncognitive skills. Information on the Big Five personality traits (openness, conscientiousness, extroversion, agreeableness and neuroticism), grit, and behaviors such as decision-making and hostility bias is gathered through a series of Likert-type items with four possible responses ranging from “almost never” to “almost always.” Items measuring these traits and behaviors are rated on a four-point Likert scale from “Almost never” to “Almost always.” Along with the skills measures described above, the STEP surveys gather extensive information on individual education and employment outcomes, labor market trajectories and on family background characteristics.

# **_Analytic sample_**

The STEP surveys are targeted to the urban working-age population (those between the ages of 15 and 64). For this study, we limited the sample to adults between the ages of 25 and 64, excluding those currently attending an educational program. The sample was further restricted to include only wage and salaried workers and those who are self-employed. Employers were excluded from the self-employed group to avoid biased estimates due to measurement error in earnings. Part-time workers (those working less than 40 hours a week) and unpaid workers were also excluded from the analytic sample, conforming to standard practice in these types of analyses. The proportion of salaried and wage workers and the self-employed group in each of the country samples is presented in Table 1.<sup>9</sup> As shown, the self-employed in Armenia, Georgia, and Ukraine constitute a much smaller group compared to those in the other countries in the sample.

> 9 Also, see Tables A.2 and A.3 in Appendix A for differences between the two worker types. These tables show that the groups are similar across measured characteristics.

9
# **Appendix 7: Data Availability**

Appendix Table 2 shows data availability by treatment status.

|**Appendix Table 2: Data**|**Availability**<br>Round 2|**by Treatm**<br>Round 3|**ent Statu**<br>R2 or R3|**s**<br>Round 4|Round 5|R4 or R5|
|---|---|---|---|---|---|---|
|**Panel A: Interviewed**|||||||
|Assigned to Training|0.034***|0.030***|0.023***|0.009|0.023*|0.010|
||(0.012)|(0.011)|(0.008)|(0.013)|(0.013)|(0.011)|
|Spillover Group|0.014|‐0.013|‐0.003|‐0.026*|0.002|‐0.009|
||(0.013)|(0.014)|(0.010)|(0.014)|(0.015)|(0.012)|
|Pure Control Mean|0.886|0.889|0.943|0.894|0.876|0.923|
|**Panel B: Data on Surviv**|**al Available**||||||
|Assigned to Training|0.011*|0.016**|0.002|0.005|0.010|0.003|
||(0.006)|(0.006)|(0.003)|(0.009)|(0.010)|(0.007)|
|Spillover Group|0.015**|‐0.003|0.002|0.000|0.008|0.004|
||(0.006)|(0.008)|(0.004)|(0.010)|(0.010)|(0.007)|
|Pure Control Mean|0.968|0.962|0.991|0.947|0.924|0.970|
|**Panel C: Data on Week**|**ly Sales and**|**Profits Av**|**ailable**||||
|Assigned to Training|0.027**|0.031***|0.016**|0.013|0.031**|0.011|
||(0.012)|(0.009)|(0.006)|(0.013)|(0.013)|(0.010)|
|Spillover Group|0.015|‐0.006|‐0.003|‐0.007|0.011|0.006|
||(0.013)|(0.012)|(0.008)|(0.014)|(0.014)|(0.011)|
|Pure Control Mean|0.907|0.913|0.964|0.903|0.881|0.939|
|Sample Size<br>Notes: Robust Standar<br>*, **, and *** indicate<br>Data on weekly sales a<br>be closed (since then s|3537<br>d Errors in P<br>significance<br>nd profits av<br>ales and pro|3537<br>arenthese<br>at the 10,<br>ailability c<br>fits are kn|3537<br>s, Clustere<br>5, and 1 pe<br>odes data<br>own to be|3537<br>d at the M<br>rcent leve<br>as availab<br>zero).|3537<br>arket Level<br>ls respectiv<br>le if firm is|3537<br>.<br>ely.<br>known to|

46
observations that it is not feasible to calculate reliable “normal” temperatures, and these stations have therefore been discarded. Only the stations that have at least eight observations for each calendar month during the reference period were included in the analysis in this paper. An additional requirement for inclusion of a data station in the present analysis is that it has at least 300 out of the 721 possible monthly observations. The 18 stations that satisfy both of these requirements are listed in Table 2.

**Table 2: Meteorological stations in Bolivia with adequate** **<u>monthly data from 1948 to 2008</u>**

|**Station**|**Latitude**|**Longitude**|**Elevation**<br>**(m)**|
|---|---|---|---|
|Camiri|20°00'S|63°32'W|798|
|Charaña|17°35'S|69°36'W|4054|
|Cobija|11°02'S|68°47'W|235|
|Cochabamba|17°25'S|66°11'W|2548|
|Concepción|16°09'S|62°01'W|497|
|La Paz / El Alto|16°31'S|68°11'W|4058|
|Oruro|17°58'S|67°04'W|3707|
|Puerto Suarez|18°59'S|57°49'W|134|
|Riberalta|11°00'S|66°07'W|141|
|Roboré|18°19'S|59°46'W|276|
|Rurrenabaque|14°28'S|67°34'W|204|
|San Ignacio de Velasco|16°23'S|60°58'W|413|
|San Joaquín|13°04'S|64°49'W|140|
|Santa Cruz Trompillo|17°48'S|63°11'W|418|
|Sucre|19°01'S|65°18'W|2904|
|Tarija|21°33'S|64°42'W|1854|
|Trinidad|14°49'S|64°55'W|155|
|Yacuiba|21°57'S|63°39'W|645|

_Source:_ NCDC’s Monthly Climatic Data for the World.

Table 3 shows the average “normal” temperatures for each month for each of these stations. It is seen that the climate differs dramatically from region to region, with Charaña in the far west being cold throughout the year due to the high elevation, while most low-land regions are hot throughout the year due to the location close to the Equator.

The difference in temperature between the warmest and the coldest month also varies. In Cobija and Riberalta, for example, the difference between the warmest and the coldest month is less than 3oC. Yacuiba is the southernmost station in Bolivia and therefore the station that shows the most marked temperature variations over the year, but the difference between the warmest and the coldest month is still only 11oC.

4
Policy Research Working Paper 5292

# **Abstract**

Alleviating poverty for the elderly requires a different approach from other age groups, and a minimum pension is likely to be the only viable option. This paper examines the impact on old age poverty and the fiscal cost of universal minimum old age pensions in 18 Latin American countries using recent household survey data. First the authors measure old age poverty rates for these countries. Then they discuss the design of minimum pensions schemes—means-tested or not—as well as the disincentives they introduce for the economic and social behavior of households including labor supply, saving and family solidarity. Finally, the authors use household survey data to simulate the fiscal cost and the impact on poverty rates of alternative minimum pension schemes in the 18 countries. They show that a universal minimum pension would substantially reduce poverty among the elderly (except in Argentina, Brazil, Chile and Uruguay where minimum pension systems already exist and poverty rates are low). Such schemes have much to be commended in terms of incentives, spillover effects and administrative simplicity, but they have a high fiscal cost. The latter is a function of the age at which benefits are awarded, the prevailing longevity, the generosity of benefits, the efficacy of means testing, and the fiscal capacity of the country.

This paper—a product of the Chief Economist’s Office, Development Economics Department—is part of a larger effort in the department to help inform development assistance and policy choices aiming to reduce poverty. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The author may be contacted at jdethier@ worldbank.org.

_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 views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
percent of enterprises reported that informal payments or gifts were requested or expected when applying for a license. The Zambia survey also asked whether licenses were needed for each consignment or whether licenses covered all consignments over a set period. About 70 percent of enterprises reported that they needed licenses for each consignment.

Export procedures can also be burdensome. For example, exporters in Mozambique need to obtain a certificate of origin, a certificate of quality, a sanitary and phytosanitary certificate and an export license, which is needed for each transaction, before exporting (Nathan Associates, 2002). The certificate for quality and the sanitary and phytosanitary both require inspections. Only the survey for Zambia asked specific questions about export licenses and permits. About 52 percent of exporters in Zambia reported that they needed export licenses, with about 90 percent of these reporting that they needed a license for each consignment (as opposed to getting a license that covered all consignments over a set period of time). The average wait for an export license was 5 days. In addition to this, about 45 percent of enterprises reported that their exports were subject to special regulations.

Many countries offer exporters special incentives, such as duty drawback schemes. Although duty drawback schemes (schemes that refund custom duties for goods that are exported) can encourage exports, their application has often been difficult in developing countries. In particular, the schemes are often complex and poorly administered, resulting in high transaction costs for firms involved in these programs.<sup>11</sup> Weak administration and fiscal problems often result in long delays before they receive refunds. For the two countries where questions were asked about delays in receiving refunds, Ethiopia and Zambia, the average waits between filing and receiving refunds were 74 and 93 days respectively.<sup>12</sup> Similar problems are common for other refunds. For example, in almost all countries with value-added taxes, including all the countries covered in this study except Ethiopia where value-added taxes had not been imposed at the time of the survey, exports are zero-rated. As a result, many exporters will

> 11 See, for example, Milner _et al_ . (2000) on Uganda

> 12 These delays can prevent firms from investing.  For example, when one firm in an Export Processing Zone in Mozambique imported equipment that should have been exempt from customs duty and VAT, it found out that it would be required to pay nearly $20,000 in duties and taxes before picking up the equipment.  The manufacturer then refused to pick up the machinery since this could tie up its working capital for up to a year (Nathan Associates, 2002).

10
The first two terms of equation (19) represent the value-added generated in _s_ and absorbed directly by the importer country _r_ without any further re-export (i.e. the ‘directly absorbed value-added in exports’, or **DAVAX** _sr_ ):<sup>11</sup>

# **Measuring Global Value Chain-related trade**

The **DAVAX** is a measure of what is produced entirely at home and consumed abroad ( **V** _s_ ( **I** _−_ **A** _ss_ )<sup>_−_1</sup> **Y** _sr_ ), and of the intermediate inputs that are (entirely) produced at home and used by the importing country to produce final goods for its internal market ( **V** _s_ ( **I** _−_ **A** _ss_ )<sup>_−_1</sup> **A** _sr_ ( **I** _−_ **A** _rr_ )<sup>_−_1</sup> **Y** _rr_ ). In this sense it identifies the ‘traditional’ type of trade/production as opposed to the international shipments that take place under the global sharing of production (‘GVC-related trade’). In other words, the ‘GVC-related trade’ includes all the traded items that cross at least two international borders, i.e. that are re-exported at least once before being absorbed in final demand. This can be considered as a sufficient condition for an exported good to be part of an international production network.

In a bilateral trade flow, the ‘GVC-related trade’ can be measured simply by excluding from country _s_ ’s gross exports the domestic value-added absorbed

> 11This indicator can be computed only in a source-based approach and differs from the sum of the first two terms in the Koopman et al. (2014) decomposition, even when summing across all the bilateral destinations. Indeed, although Koopman et al. (2014) refer to these terms as ‘exports absorbed by direct importers’, in Section 5.2 we show that these subcomponents are not correctly identified in their decomposition.

18
for Colombian firms, this provides us with sufficient external validity to explore their trade response to market access conditions in LAC. Third, the importance of East Asian and Pacific destination markets for Colombian exporters has doubled between 2007 and 2017 to 10% of total exports now.

Table 1: Colombian exports (2007 base) and selected destinations

|Destination|**2007**|**2008**|**2009**|**2010**|**2011**|**2012**|**2013**|**2014**|**2015**|**2016**|**2017**|
|---|---|---|---|---|---|---|---|---|---|---|---|
|World|1.00|1.25|1.10|1.36|1.97|2.08|2.00|1.85|1.22|1.04|1.27|
|East Asia & Pacific|0.05|0.04|0.05|0.08|0.06|0.09|0.11|0.13|0.11|0.08|0.10|
|EasternEurope|0.01|0.01|0.01|0.01|0.00|0.00|0.00|0.00|0.01|0.01|0.01|
|Latin America & Caribbean|0.37|0.39|0.31|0.29|0.33|0.32|0.32|0.31|0.36|0.34|0.37|
|Middle East & N. Africa|0.01|0.01|0.01|0.01|0.01|0.01|0.01|0.01|0.01|0.01|0.01|
|North America|0.37|0.40|0.41|0.44|0.40|0.37|0.33|0.28|0.29|0.35|0.30|
|SouthAsia|0.00|0.00|0.02|0.02|0.01|0.02|0.05|0.05|0.02|0.01|0.01|
|Sub-Saharan Africa|0.00|0.00|0.00|0.01|0.01|0.01|0.00|0.01|0.01|0.01|0.00|
|Western Europe|0.19|0.16|0.19|0.15|0.18|0.17|0.18|0.20|0.20|0.20|0.19|

Source: Author’s calculation based on the original Colombian customs data. Note: This table displays Colombian exports as a share of total exports and is normalized in 2007.

Since NTM data are only available for LAC countries, the focus of the paper is on Colombian exports to LAC countries. Thus, the following tables explore how export margins have evolved, taking LAC countries as the destination. Table 2 reveals that the number of firms exporting to LAC countries followed a U-shaped pattern over the period under investigation. The number of firms decreased by about 10% during the global financial crisis of 2007–2010 and then steadily increased to reach a new peak in 2017. In relative terms, however, the importance of LAC firms remained stable at around 68% of all exporting firms over the whole period. Moreover, the importance of firms only exporting to LAC destination markets remained stable between 2007– 17. Indeed, both the number and the corresponding share of firms exporting to LAC countries exclusively wavered at around 3,500 firms or 44% of all exporting firms in Colombia during the whole period, respectively.

Looking at the product diversification of Colombian firms yields three insights. First, multi-product firms dominate Colombian exporters. They make up 91% of all exporting firms, a share which remained remarkably stable between 2007–17. Second, Table 3 reveals that Colombian exporters increased the diversification of their product portfolio from an average of 9
An example of the power of services reform is offered by Indonesia. Duggan, Rahardja, and Varela (2013) examined the extent to which policy restrictions on foreign direct investment in the Indonesian service sector affected the performance of manufacturers over the period 1997– 2009. They used firm level data on manufacturers’ total factor productivity and the OECD’s foreign direct investment Regulatory Restrictiveness Index, combined with data from Indonesia’s input‐output tables regarding the intensity with which manufacturing sectors use services inputs. Controlling for firm‐level fixed effects and other relevant policy indicators, they find, first, that relaxing policies toward foreign direct investment in the service sector was associated with improvements in perceived performance of the service sector. Second, they found that this relaxation in service sector foreign direct investment policies accounted for 8 percent of the observed increase in manufacturers’ total factor productivity over the period. The total factor productivity gains accrued disproportionately to firms that are relatively more productive, and gains are related to the relaxation of restrictions in both the transport and electricity, gas, and water sectors. Total factor productivity gains were associated, in particular, with the relaxation of foreign equity limits, screening, and prior approval requirements, but less so with discriminatory regulations that prevent multinationals from hiring key personnel abroad (Figure 14).

Figure 14. Indonesia: Services FDI reform and productivity in the manufacturing industries <!-- Start of picture text --> a. Services FDI reform<br><!-- End of picture text -->

- b. Productivity gains from services reform

<!-- Start of picture text --> 2010 2006 2003 1997 Lower Bound (90% Conf.)<br>Mid Point Estimate<br>Overall FDI Index Upper Bound (90% Conf.)<br>All Services<br>Real estate investment<br>Business services All<br>Financial services<br>Communications<br>Media<br>Hotels & restaurants<br>Transport<br>Distribution Domestic<br>Construction<br>Electricity<br>0.0 0.2 0.4 0.6 0.8 1.0 ‐0.2 ‐0.15 ‐0.1 ‐0.05 0<br><!-- End of picture text --> Source: Duggan, Rahardja and Varela (2013)

# **iii. Services openness and comparative advantage**

Since a large part of goods trade includes trade in embodied services, the development of the domestic services sector and access to imported services inputs can be expected to influence comparative advantage in manufacturing trade. The impact of services development on manufacturing trade is not straightforward. Since services are used as inputs in the production effect of substantial FDI inflows in producer services sectors on the total factor productivity of Chilean manufacturing firms. Their findings also suggest that services FDI fosters innovation in manufacturing and offers opportunities for laggard firms to catch up with industry leaders. These benefits arise not just from foreign investment but also from cross‐border trade in services. For example, Amiti and Wei (2009) find that services offshoring by high‐income countries tends to raise their manufacturing sectors’ productivity.

29
Inequality, Economic Growth and Nonrenewable Resources

- Robinson, James A., Ragnar Torvik, and Thierry Verdier. 2006. “Political Foundations of the Resource Curse.” _Journal of Development Economics_ 79(2):447–68. Retrieved (http://linkinghub.elsevier.com/retrieve/pii/S0304387806000137).

- Sachs, Jeffrey D. and Andrew Warner. 1995. _Natural Resource Abundance and Economic Growth_ . Cambridge, MA. Retrieved (http://www.nber.org/papers/w5398.pdf).

- Sachs, Jeffrey D. and Andrew M. Warner. 1999. “The Big Push, Natural Resource Booms and Growth.” _Journal of Development Economics_ 59(1):43–76. Retrieved (http://linkinghub.elsevier.com/retrieve/pii/S030438789900005X).

- Sachs, Jeffrey D. and Andrew M. Warner. 2001. “The Curse of Natural Resources.” _European Economic Review_ 45(4-6):827–38. Retrieved

- (http://linkinghub.elsevier.com/retrieve/pii/S0014292101001258).

- The World Bank. 2012. _Inclusive Green Growth_ . The World Bank. Retrieved (http://elibrary.worldbank.org/doi/book/10.1596/978-0-8213-9551-6).

19
# **2. Accuracy of the Cross-Country Raw Dataset**

In order to have a sense of the reliability of the raw data in each of the countries included in the Database, we apply two filters: a) For the first filter we compare the total values exported (excluding HS 27) calculated from the cross-country raw dataset with the total values exported from the United Nations’ COMTRADE database (excluding HS 27) for every country and year.<sup>26</sup> This comparison yields quite different results for different countries. On the one hand, for Albania, Brazil, Cameroon, Chile, Colombia, Costa Rica, Ecuador, Guatemala, Kenya, Lebanon Mexico, Morocco, Pakistan, Peru, South Africa, Tanzania and Turkey, the ratio of total values exported in the raw dataset to total values exported in COMTRADE is about 100 percent. On the other hand, for Mali and Yemen, the ratios indicate that total values exported in the raw dataset are as low as half of the total values exported in COMTRADE. On the opposite end, for Mauritius total values exported in the raw dataset are on average 30 percent above total values exported in COMTRADE.<sup>27</sup> Appendix 2 provides detailed results on these comparisons.

b) For the second filter, we focus on the countries and years that would have been left out because of a unfavorable match with COMTRADE (below 60 percent) and we keep those countries (and years) where we observe internal consistency within the export totals calculated from the corresponding exporter-level raw dataset over time. Although both sources of trade data, COMTRADE as well as the exporter-level raw datasets that we use, originate from customs authorities, we are aware of potential difficulties in the processing of the exporter-level

> 26 Given our understanding about which transactions are included in the raw files, for this comparison we consider gross export figures in COMTRADE (which include re-exports) for Albania, Kenya, Mauritius, Tanzania and Senegal and net export figures (gross exports minus re-exports) for Jordan, Uganda and Yemen. For all other countries, it does not matter which COMTRADE figure we choose as they report either insignificant or no re-exports.

> 27 For Mauritius we examined the discrepancy further at the country-HS 2-digit-year level and found that the average match percentage benefited significantly from the exclusion of observations belonging to Chapter 49. Similarly, for Cambodia and Macedonia the average match percentage benefits significantly from the exclusion of observations belonging, respectively to Chapter 49 and Chapter 62.

13
<!-- Start of picture text --> Figure 4b. Poverty headcount ratio<br>After top-up transfer (with change in poverty headcount ratio)<br>Headcount among 60+ (no means test) Change in headcount (no means test)<br>Headcount among 60+ (means-tested) Change in headcount (means-tested)<br>Poverty line = $2.5 a day, OECD equivalency scale adjusted household size<br>Argentina Bolivia Brazil ChileColombiaCosta RicaDominican RepublicEcuadorEl SalvadorGuatemalaHonduras MexicoNicaraguaPanamaParaguay PeruUruguayVenezuela, RB<br>0.107 0.107<br>0.102<br>0.097<br>0.089<br>.1 0.081<br>0.075<br>0.071<br>0.064<br>0.053<br>0.043 0.045 0.049<br>.05 0.029 0.035 0.029 0.031 0.028 0.034<br>0.016 0.019 0.023 0.014 0.014 0.018 0.020<br>0.005 0.006 0.004 0.006 0.001 0.003 0.006 0.007 0.000 0.000<br>0<br>-0.009 -0.007 -0.003 -0.001 -0.009 -0.008 -0.001 -0.001<br>-0.024 -0.023 -0.019<br>-0.030<br>-.05<br>-0.053 -0.050 -0.055 -0.050 -0.046 -0.055 -0.051<br>-0.073 -0.067 -0.069 -0.072 -0.066<br>-.1 -0.082 -0.079<br>-0.088 -0.090<br>-0.106 -0.109 -0.108 -0.107 -0.102<br>-0.117<br>-.15 -0.128 -0.125<br><!-- End of picture text --> 33