rafmacalaba/data-use-mentions-extended huggingface dataset:
The paper is structured as follows. Section 2 explains our data sources and procedures to clean them. Section 3 documents stylized facts about Colombia’s exporting firms and the market access conditions they face. Section 4 explains our empirical identification strategy, defines key variables, and addresses concerns to identify how market access conditions affect Colombian firms trading with the region. The results and robustness tests are presented in Section 5. Sections 5.5 and 5.6 explore heterogeneous NTM effects based on firms’ size and participation in global value chains, respectively. Finally, Section 6 concludes and discusses the implications of our results.

# **2 Data**

The empirical investigation is based on three distinct datasets, all covering the period 2007–2017. The first dataset contains information on export and import transactions collected by the Colombian customs. The second provides data on NTM types applied by Latin American importing countries. The third contains bilateral applied ad-valorem tariffs of Latin American importing countries.

## **2.1 Firm-Level Customs Data**

We use transaction-level export and import data from Colombia’s customs agency DIAN. The data is part of the expansion to the Exporter Dynamics Database, as described in Fernandes et al. (2016). The data cover the universe of exporting firms in all sectors at the exporter-HS 6-digit productdestination-year level and includes seven variables: country of origin, exporting firm identifier, country of destination, HS 6-digit product, export value, export quantity, and year. Information on export and import values is expressed in US dollars and is FOB (free on board). We processed the raw dataset to a series of cleaning procedures, as detailed in Cebeci and Fernandes (2015).

To merge with the tariff variable described below, we convert the HS 6
2020 (Solomon Islands and Vanuatu) and five surveyed in 2022 (Indonesia, the Philippines, the Lao People’s Democratic Republic, Vietnam, and Mongolia). These countries were chosen to provide a range of middle-income education systems in EAP and for which the team was able to hire enumerators and get permission to collect data from relevant ministries. We compare data from these countries with seven other participating countries of either the 2020 survey or the 2022 survey if the question item was present in both surveys. The non-EAP country participants in the 2022 survey were officials in the Democratic Republic of Congo (DRC), Ghana, Peru, Pakistan, Bangladesh, Nigeria, and Uganda. The list of participating countries for both surveys can be found in Table 1.

# 2 Methodology

The survey design and data collection were conducted by the World Bank together with the CGD. The full survey and data are available, along with a complete description of the methodology (CGD, forthcoming). This survey, conducted in 2022 together with CGD, builds on earlier surveys of public officials on their priorities and beliefs about the education sector in their countries (Crawfurd et al., 2021). A summary of the methodology is presented here.

> 1 This paper is a double companion paper; it is a companion paper both to the regional flagship report “Fixing the Foundation: Teachers and Basic Education in East Asia Pacific” (2023) and a companion paper to CDG’s global analysis of the 2022 survey, also forthcoming.

> 2 This paper focuses on areas not already covered in detail in the EAP Education Flagship (Afkar et al., 2023).

1
times as high as in Slovenia, and in Croatia the rate is 2.5 times as high as in Slovenia. Figure 8 illustrates this huge dispersion in unemployment rates in ECA.

# **Figure  8  Unemployment rates vary strongly across transition economies**

## **Unemployment rate, 2002**

<!-- Start of picture text --> 35<br>30<br>25<br>20<br>15<br>10<br>5<br>0<br>Source: ILO LABORSTA database (LFS data)<br>Macedonia, FYR _SEEPolandSlovakiaBulgaria BIHCroatiaSerbia and MontenegroLithuania Georgia Latvia _CEEEstoniaUkraineRussiaRomaniaCzech Republic_EU15 _OECDMoldovaSloveniaHungary<br>percent<br><!-- End of picture text --> 21
<!-- Start of picture text --> Figure 1: Internet users in Egypt<br>8 46.9<br>45.0<br>41.2<br>e 37.8<br>a 33.9<br>E 29.4<br>28<br>3 25,5 26.4<br>c=<br>5 20.0 21.6<br>se 18.0 wm<br>ec 16.0<br>17<br>i1.g 128<br>2<br>27 4.0<br>o6 0.8<br>o<br>2000 2001 2002 2003 2M 20065 2006 S007 S008 2008 2010 2011 2012 32013 2014 B015 2016 a017 2018<br>Source: ITU World Telecommunication/ICT Indicators database (2019)<br><!-- End of picture text -->
- <u>Greenan, N., and D. Guellec (2000). “Technological Innovation and Employment Reallocation.”</u> _Labour_ 14(4): 547–90.

- <u>Gregg, P. and J. Wadsworth (2002). “Job tenure in Britain, 1975–2000. Is a job for life or just for</u> Christmas?” _Oxford Bulletin of Economics and Statistics_ , 64: 111-134.

- <u>Hanoch, G. and M. Honig (1985). “‘True’ Age Profiles of Earnings: Adjusting for Censoring and</u> for Period and Cohort Effects.” _Review of Economics and Statistics_ , 67(3): 383-394.

- <u>Harrison, R., Jaumandreu, J., Mairesse, J., and B. Peters (2014). “Does Innovation Stimulate</u> Employment? A Firm-Level Analysis Using Comparable Micro-Data from Four European Countries.” _International Journal of Industrial Organization_ 35: 29–43.

- <u>Heckman, J. and R. Robb (1985). “Using Longitudinal Data to Estimate Age, Period and Cohort</u> Effects in Earnings Equations.” In _Cohort Analysis in Social Research beyond the Identification Problem._ Editor: Mason, W. M., Fienberg S. E. New York: Springer.

- <u>Hollister, M. (2011). “Employment Stability in the U.S. Labor Market: Rhetoric versus Reality”</u> _Annual Review of Sociology_ 37(1): 305-324.

- <u>Hollister, M. N. and K. E. Smith (2014). “Unmasking the Conflicting Trends in Job Tenure by</u> Gender in the United States, 1983–2008.” _American Sociological Review_ , 79 (1): 159-181.

- <u>Hyatt, H. and J. Spletzer (2013
> Almost one in ten firms (9 percent) is active in producing agricultural products,<sup>10</sup> while less than one in twenty (4 percent) is manufacturing products.

Formal enterprises are defined as legally registered either at the national registrar general or country-level revenue authority, at the local authority or municipality, or both. Informal enterprises have neither national nor local registration. Most firms are informal: more than two-thirds (73

> 6 The ranking in terms of number of observations per country, from highest to lowest, is Nigeria (567), Senegal (517), Tanzania (500), Ghana (499), Mozambique (430), Kenya (421), and South Africa (391).

> 7 More than half of Uganda data had missing values for sales (necessary to calculate two of the performance variables); and the age of the owner was missing for 92 percent of observations. There was no information on the export status of enterprises for Rwanda (whether they had customers abroad, a performance variable as well), as well as on owner’s age, whether the enterprise had non-local buyers, and whether the manager has vocational training.

> 8 In the survey, firms were asked whether they are producing agricultural products, manufacturing products, selling goods/trading, and/or providing other services. Roughly 11 percent of respondents indicated that they do more than one of these, for instance producing vegetables as well as selling them as traders.

> 9 Other services include activities such as basic tailoring and more sophisticated fashion design services, hairdressing and other beauty services, hotel and restaurant services, mechanical and repair services, transportation services, as well as mobile money transfer services.

10 Agricultural products include vegetables (e.g., cabbage, cassava, tomatoes), crops (e.g., cocoa, maize, rice, sugarcane), livestock (e.g., cattle, chickens, goats, sheep), and fishing
in spatial and radiometric resolution, spectral responses, the spread function of the sensors, local overpass time at night, radiance range, and on-board calibration (Li et al. 2017; Sahoo, Gupta and Srivastav 2020). Furthermore, the DMSP-OLS sensor measures night light density annually in digital numbers (DN), while the VIIRS sensor measures monthly in radiance (Gibson et al. 2021).

> 4The authors follow four steps to inter-calibrate DMSP-OLS and VIIRS sensors: DMSP-OLS calibration, the annual composition of VIIRS, and VIIRS conversion like DMSP-OLS. In the first step, the authors calibrated the stable DMSP-OLS night lights from 1992 to 2013. This calibration process aimed to ensure consistency and accuracy in the DMSP-OLS data. In the second step, the authors addressed the noise present in the VIIRS data caused by factors such as clouds, auroras, and temporary lights like fires and boats. They applied noise-removal techniques to improve the quality of the VIIRS data. In the third step, the authors converted the higher resolution of VIIRS (15 arc-seconds) to match the resolution of DMSP-OLS (30 arcseconds) using the kernel density approach, which is similar to the method described in Li et al. (2017). This conversion ensured consistency between the two datasets. Finally, to convert the processed data into digital numbers (DN), the authors employed a sigmoid function proposed by Zhao et al. (2019). As a result, it is possible to use consistent and calibrated night light data.

13
**Figure 23** plots the annual average growth rate of public consumption (in real terms across moderate borrowers in the region during the period 2012-17. Note that with the exception of Nigeria, public consumption grew during the period under analysis for all moderate borrowers. The median annual growth rate for these 16 countries is 5.2 percent. Five moderate borrowers have an annual growth rate of public consumption that exceed 8 percent (Niger, Guinea, Rwanda, Burkina Faso and Ethiopia).

**Figure 24** shows the annual evolution of investment growth by group of borrowers from 2013 to 2017. Interestingly, there are divergent trends in investment growth across country groups: (a) there is a steady increase in the (cross-country) median rate of growth of investment of light borrowers from about 2 percent in 2013 to 7 percent in 2017. (b) Investment growth by moderate borrowers declines up to 2015 (partly attributed to the decline in commodity prices and recovers swiftly (to 6-6.5 percent in 2016-17). (c) After the plunge in commodity prices in 2014-5, the growth of investment not only decelerates sharply but it does not recover in the subsequent years. This implies that the investment growth collapse is more marked among heavy borrowers after the sharp commodity price decline.

**Figure 24. Investment growth by groups of borrowers in Sub-Saharan Africa, 2013-17**

<!-- Start of picture text --> 14<br>12<br>10<br>8<br>6<br>4<br>2<br>0<br>2013 2014 2015 2016 2017<br>-2<br>-4<br>Light borrowers Moderate borrowers Heavy borrowers<br>Percent per year<br><!-- End of picture text --> _Source: World Bank, World Development Indicators. Elaboration: AFRCE Staff estimates._ 28
057]|[0.001]|[0.089]|[0.505]|[0.232]|[0.011]|
|First Stage F-Statistic|6.22|5.79|5.79|5.79|6.30|1.99|6.74|
|N|13,909|13,755|13,755|13,755|13,789|24,649|24,649|
|||||G. Using 1994 Census|Start Ages|||
|�<br>Years of Schoolingizy|0.083|0.014|0.112|-0.028|-1.326|-1.333|-1.605|
||(0.056)|(0.029)|(0.048)|(0.045)|(2.144)|(0.675)|(0.794)|
||[0.139]|[0.613]|[0.020]|[0.531]|[0.536]|[0.048]|[0.043]|
|First Stage F-Statistic|1.23|1.36|1.36|1.36|1.49|3.45|18.70|
|N|13,909|13,755|13,755|13,755|13,789|24,649|24,649|
|||||H. Three-Part Distr|ict Trends|||
|�<br>Years of Schoolingizy|0.082|0.090|0.020|-0.008|-0.648|-0.728|-0.746|
||(0.060)|(0.024)|(0.046)|(0
the country series for those indicators that are more sensitive to the specific change. The large majority of the studies that are not country specific do not mention how they deal with the effect that the changes in the survey features have on the comparability of their estimates. We break with this tradition and work on four different areas, which are described below.

First, we use unique information about the comparability of surveys on the same country in terms of income aggregates (see table A.1) to define circa years (see table B.1) that minimize the measurement error of

> 14 For more information, see the SEDLAC Project webpage, at http://sedlac.econo.unlp.edu.ar/eng/.

> 15 The SEDLAC dataset allows cross-country comparisons of household welfare. The data cover 17 countries, which account for 90 percent of the population in the region. The countries are Argentina (urban only), Bolivia, Brazil, Chile, Colombia, Costa Rica, the Dominican Republic, Ecuador, El Salvador, Guatemala, Honduras, Mexico, Nicaragua, Panama, Paraguay, Peru, and Uruguay (urban only). See annex A for more details on the specific combination of country-year surveys included in the analysis. See SEDLAC (SocioEconomic Database for Latin America and the Caribbean), Center for Distributive, Labor, and Social Studies, Universidad Nacional de La Plata, La Plata, Argentina and World Bank, Washington, DC, http://sedlac.econo.unlp.edu.ar/eng/statistics.php.

4
The business environment has not helped Brazil weather stronger competition in global markets. The logistics infrastructure, which is widely recognized as inefficient and costly to Brazilian exports, as well as many other factors, such as a very complex and costly tax system, have taken a toll on Brazilian firms. This diagnostic reinforces the importance of resuming the agenda of microeconomic reforms, increasing the investment-to-GDP ratio, and advancing toward a better-skilled human capital base. Promoting and rewarding productivity gains in a competitive economy, including the service sector, are the only options to accelerate growth and overcome possible middle-income growth traps (Agenor and Canuto 2012).

# **References**

- Agenor, P.-R., and O. Canuto. 2012. “Middle-Income Traps.” World Bank Policy Research Working Paper 6210, Washington, DC.

- Bernard, A. B., and J. B. Jensen. 1999. “Exporting and Productivity.” National Bureau of Economic Research Working Paper 7135.

- Besedes, T., and T. Prusa. 2006. “Ins, Outs and the Duration of Trade.” _Canadian Journal of Economics_ . 39 (1): 266–95.

- Bonelli, R., and A. C. Pinheiro. 2012. “Competitividade e Desempenho Industrial: Mais Que só Câmbio.” XXIV Fórum Nacional Rumo ao Brasil Desenvolvido.

- Cebeci, T., A. M. Fernandes, C. Freund, and M. D. Pierola. 2012. “Exporter Dynamics Database.” Banco Mundial, 22 de maio, preliminar.

- Clerides, S., S. Lach, and J. Tybout. 1998. “Is Learning by Exporting Important? Micro-Dynamic Evidence from Colombia, Mexico and Morocco.” _Quarterly Journal of Economics_ 113 (3): 903–47.

- Corden, W., and P. Neary. 1982. “Booming Sector and De-Industrialization in a Small Open Economy.” _Economic Journal_ 92 (December): 825–48.
Notes: Data pooled for all follow-up surveys, unless otherwise noted. Specifications include strata dummies, a variable representing the initial outcome at baseline, a variable indicating missing data at baseline, and dummies for survey round. Z score index constructed following Kling, Liebman, and Katz (2007). Adjustments to control false discovery rate (FDR) computed following Benjamin and Hochberg (1995). p-values and q-values are reported below standard errors (q-values in italics). Clustered standard errors by firms in parentheses. *, ** and *** denote significant at the 10%, 5%, and 1% levels, respectively.

a Z-score on inspection and harassment from state includes business has not received an inspection from municipality, taxes authority or another inspector during the past 6 months, owner has not experienced requests for bribes in the past 6 months, owner is confident to say no to bribes, business has not received threats to shut down, business has not received threats to confiscation of property or merchandise, owner has not been sexually harassed or has not received any other harassment.

b Z-score on financial services includes six variables. Those are owner has a bank account, has a business bank account, owner use an account for business purposes, owner does not save at hombre or ROSCA or SACCO, owner saves at bank.

c Sample size of z-score on financial services is 9,438 because businesses which have closed during the program are excluded. For several savings outcomes for which it is possible that individuals are saving even without operating a business, we use the sample of firms still in business since we lack data on these outcomes for those whose businesses have closed.

40
gas pipeline (Camisea) has a minimum revenue guarantee for the service provision. The guarantee is financed through a fee included in electricity tariff —assuming that those who consume electricity benefited the most from cheaper natural gas in power production. No taxpayer money is involved in the payment. If private agendas in politics are a concern, this mechanism would help limit the risk of corruption and reduce potential biases in sector decisions at the government level—particularly those that could reduce commitment to the revenue guarantee.<sup>37</sup>

# **3.4. Corruption: Maximizing personal wealth illegally**

While the empirical evidence for political corruption is scarce, the incentives and potential impacts of such criminal acts when they happen are more predictable. Moreover, it might be difficult to single out corruption as the explanatory factor behind a given weakness in sector performance. By intuition, corruption will affect performance differently than the other agendas discussed, including decisions about market structure, privatization, and modes of service provision. It is particularly likely that corruption in sector governance will weaken the function of sector oversight systems.<sup>38</sup> Despite the data difficulties, there is a growing volume of empirical evidence of correlations between corruption and performance in the utility sector.<sup>39</sup> However, most results describe corruption at the bureaucratic level, often referred to as “petty corruption” (for an overview, see Clarke and Xu, 2002; Kenny 2006, Dal Bó and Rossi, 2007). One of the most thorough surveys of corruption, by Hellman et al., (2000), finds that petty corruption and political corruption tend to coexist. Other empirical studies point at the links between general perceptions of corruption and sector performance. According to Estache (2006a) corruption reduces sector performance in all the utility sectors. The impacts of political and bureaucratic corruption are difficult to separate, however.

Political corruption is related to industry-friendliness and other dimensions of rent-seeking. Even if few
and the UK during the 1985-2011 period, Colombelli et al. (2020) show a positive impact of innovation on the market value of firms. They also find that firms operating in sectors with a high propensity for green technologies yield a significant positive effect along with the stock of green technologies. Based on the financial performance data of 420 energy firms in OECD countries over four years (2013-2016), Hassan (2019) shows that green investment promoted by renewable energy policies in OECD countries improves firms’ financial performances.

37
Consistent with diminishing marginal returns and the convergence hypothesis of the neoclassical growth theory, all the statistically significant coefficients on the ―convergence factor‖ (3-lag value added per worker) are negative, ranging from -0.038 for the mining sector to -0.018 for wholesale and retail sector. The coefficient on the ―convergence factor‖ for the construction sector is -0.015, but not significant at the 10 percent level, indicating that construction industry does not exhibit statistically significant diminishing returns.

The coefficients on banking and currency crises are negative in all the specifications; however, the sectors exhibit different degrees of sensitivity to such episodes. Currency crises have the most adverse impact on construction sector growth (coefficient of -0.04), while public utilities sectors are most affected by banking crises followed by the manufacturing sectors (coefficients of approximately -0.03). The coefficients on remaining controls are generally consistent with theory. Inflation and government spending exhibit a negative association with sectoral value added growth, while trade openness has a positive association.<sup>5</sup> Interestingly, the coefficient on de-facto financial openness is only significant for the construction sector regression, and is negative and significant at the 1 percent level.

# Table 6 [about here]

Table 6 reports analogous regression results with the additional set of controls for agricultural versus industrial shares, and institutional quality of each economy. The results discussed above are robust to the inclusion of additional controls, with the coefficient on lagged financial growth in the construction sector regression exhibiting a higher magnitude than in the baseline specification (0.22 compared to 0.17).

# **ii. Financial shocks**

Tables 7 and 8 report panel regression results based on specification (6) with lagged financial value added growth replaced by the financial sector contraction and expansion dummy

> 5 The data on government spending obtained from Heston, Summers and Aten (2009), which includes collective consumption of government for public good type activities, like police
others, discussing business matters, and making decisions together is an inexpensive alternative. By exchanging ideas and<br>cooperating with others, you practically benefit for free.|
|**Step-by-step Implementation Guidance**<br>Before we start: Topics to discuss or decide on together. In our survey of 1300 shop owners in Jakarta, we found that many already discuss business matters with<br>their spouses, friends, or suppliers they trust. Most use the opportunity to exchange ideas about sales, selling and purchasing prices, as well as which products<br>are most profitable or sell best. They discuss which products to start offering for sale and where to acquire additional capital (e.g., bank offers or government<br>programs). Moreover, shop owners report discussing discount policies (which discounts to give, to whom, when, and how much), product quality, reliability of<br>suppliers, marketing and promotional offers, and general market trends. Beyond these topics, we strongly recommend discussing business practices.|
|Step 1: Observe or discuss expertise and define clear roles. The first step in finding out what could be worth discussing apart from the above mentioned topics is<br>to see where you and your co-worker/spouse differ in expertise. Once you know where you differ, you have identified a good discussion topic, you are sure to<br>complement each other’s knowledge.|
|Step 2: Dedicate time for quick feedback. Once you have thought about topics to discuss, whom to discuss them with, and have assigned responsibilities so as to<br>benefit from everyone’s expertise, it is a good idea to set out a routine for feedback. It is not easy to break old habits, and so reserving a specific time of the day,<br>preferably towards day’s close when you make plans for stocking up, for feedback can help. Just take a few minutes every
|
|Received a Pamphlet*Price||||0.068|0.060|0.111|
|||||(0.280)|(0.267)|(0.266)|
|Observations|2,925|2,925|2,925|2,925|2,925|2,925|
||Without|Price Interac|tion: Eq 2|With Pr|ice Interaction|: Eq 4|
|Received a Pamphlet|-0.038|-0.030|-0.033|0.030|0.041|0.033|
||(0.024)|(0.023)|(0.023)|(0.072)|(0.071)|(0.070)|
|Received a Pamphlet*No-Contract|0.076**|0.070**|0.075**|-0.047|-0.052|-0.052|
||(0.034)|(0.032)|(0.032)|(0.102)|(0.098)|(0.098)|
|Observations|2,925|2,925|2,925|2,925|2,925|2,925|
|No Contract treatment efect|0.038*|0.040*|0.042*|-0.017|-0.011|-0.019|
|RP+(Rp*No contract)|[0.096]|[0.076]|[0.057]|[0.804]|[0.868]|[0.778]|

Source: RUMiC Migrant Survey 2015 and 2016, RUMiC Information Intervention Field Experiment, 2016. Notes: The additional variables included in Model 1 are a vector of city fixed