rafmacalaba/datause-encoder-data huggingface dataset:
- Boussard, Jean-Marc, Francoise Gerard, Marie Gabrielle Piketty, Mourad Ayouz, and Tancrède Voituriez. 2006. “Endogenous Risk and Long Run Effects of Liberalization in a Global Analysis Framework.” _Economic Modelling_ 23 (3) (May): 457–475.

- Brainard, William C, and Richard N Cooper. 1968. “Uncertainty and Diversification in International Trade.” _Food Research Institute Studies_ 8: 257–285.

- Von Braun, Joachim, and Maximo Torero. 2009. “Implementing Physical and Virtual Food Reserves to Protect the Poor and Prevent Market Failure.” _IFPRI, Policy Brief_ 10 (February).

- Brinkman, Henk-Jan, Saskia de Pee, Issa Sanogo, Ludovic Subran, and Martin W Bloem. 2010. “High Food Prices and the Global Financial Crisis Have Reduced Access to Nutritious Food and Worsened Nutritional Status and Health.” _The Journal of Nutrition_ 140 (1): 153S–161S.

- Brock, William A, and Cars H Hommes. 1997. “A Rational Route to Randomness.” _Econometrica_ 65 (5) (September): 1059–1096.

- Cafiero, Carlo, Eugenio S A Bobenrieth, Juan R A Bobenrieth, and Brian D Wright. 2011. “The Empirical Relevance of the Competitive Storage Model.” _Journal of Econometrics_ 162 (1) (May): 44–54.

- Cassing, J H, A L Hillman, and N V Long. 1986. “Risk Aversion, Terms of Trade Uncertainty and Social-Consensus Trade Policy.” _Oxford Economic Papers_ 38 (2): 234–242.

- Chapoto, Antony, and Thomas S Jayne. 2009. “The Impacts of Trade Barriers and Market Interventions on Maize Price Predictability: Evidence from Eastern and Southern Africa.” _Michigan State University, Department of Agricultural, Food, and Resource Economics, Food Security International Development Working Papers_ 56798 (December).

- Coleman, Andrew. 2009. “Storage, Slow Transport, and the Law of One Price: Theory with Evidence from Nineteenth-Century U.S. Corn Markets.” _Review of Economics and Statistics_ 91 (2) (May): 332–350.

- Crosby, Daniel. 2008. “WTO Legal Status and Evolving Practice of Export Taxes.” _Bridges_ 12 (5): 3–4.

- D’Souza, Anna, and Dean Jolliffe. 2012. “Rising Food Prices and Coping Strategies: Household-level Evidence from Afghanistan.” _Journal of Development Studies_ 48 (2): 282–299.

- Dawe, David. 2001. “How Far down the Path to Free Trade? The Importance of Rice Price Stabilization in Developing Asia.” _Food Policy_ 26 (2) (April): 163–175.

Demeke, Mulat, Guendalina Pangrazio, and Materne Maetz. 2009. “Country Responses to the Food Security Crisis: Nature and Preliminary Implications of the Policies Pursued”. Initiative on Soaring Food Prices, FAO.

39
16 year fixed effects (FE). More precisely, for countries _c_ and years _t_ , we estimate: URB _c,t_ = _α_ + _β_ NRXGDP _c,t−_ 1 + _γ_ MFGSERV _c,t_ + _δ_ DEINDU _c,_ 1980 _−t_ + _κc_ + _θt_ + _Xc,tπ_<sup>_′_</sup> + _μc,t._ (9) We use NRXGDP in the previous period _t_ -1, as we expect rents from resource exports to have a lagged effect on urbanization; it takes time to build cities. MFGSERV is defined in _t_ since the production of MFG and SERV mostly takes place in cities, where they have a contemporaneous effect on the urban share. The deindustrialization variable – DEINDU – measures the absolute decline in the GDP share of manufacturing between 1980 and _t_ transformed so that a positive value indicates _more_ deindustrialization, while 0 is assigned if no decline is observed or if _t ≤_ 1980. Finally, we include the time-varying controls (log population and its square) and standard errors (SEs) are clustered at the country level.

In cols. (2), (3) and (4), we consider data every 20 years, 10 years, and 5 years, respectively. As seen in Panel A, the panel estimates of NRXGDP and MFGSERV are smaller than their corresponding long-difference estimates in col. (1). The point estimates of NRXGDP are lower for shorter time periods in the panel and in all cases, they are smaller in magnitude than the point estimates for MFGSERV. These results are expected given the delayed effect of increases in NRXGDP on urbanization. Lastly, DEINDU has a small positive impact on urbanization. DEINDU countries are countries that saw their MFGSERV share increase in the past, which might still generate urbanization in the shorter run as urbanization begets urbanization. Once we control for the urban share in _t_ -1, DEINDU’s coefficient is close to 0 (0.01 with the 5-year panel; not shown).

**Timing.** With country fixed effects, identification comes from _within-country_ changes in NRXGDP, MFGSERV, and DEINDU. Web Appx. Table D.3 examines how urbanization correlates with the timing of such changes. We use the 10-year panel specification (9) and include leads and lags. The table shows that leads of the variables are small and not significant. In contrast, some lags are significant. For MFGSERV, all lags have significant coefficients. For NRXGDP, the second and third lags have larger and more significant coefficients, suggesting that urban shares increase 20-30 years after resource booms. Urbanization thus seems to follow industrialization/FIRE-ization and resource export booms, not the other way around. No significant correlation is observed for DEINDU.

**Additional results** In Panel B of Table 2, we decompose NRXGDP into the export-toGDP ratio of fuel & mining (FMXGDP) and agricultural products (AGXGDP). The long-
<!-- Start of picture text --> INPUT COMPONENT TARGET GOAL<br>State sponsored Foster Increase Poverty<br>SME Loans Entrepreneurship Employment Reduction<br>Input Indicator: Output Indicator: Outcome Indicator : Impact Indicator :<br>Provide 100 M $ for low Increase the Entry rate Reduce unemployment Reduce population under<br>interest loans for entrepreneurs by 15% by 5% poverty line by 3%<br>Intermediate Indicators Final Indicators<br><!-- End of picture text --> This example highlights that governments without a proper information-mechanism to measure entrepreneurship lack a valuable tool that could help them to better design, implement, and evaluate their programs.

# **_3.6 Challenges to the Development of Business Registries_**

Business registries have developed at a different pace across countries due mainly to two formidable challenges:

# _a) Modernization of public administration:_

An innovative public administration with a digital modernization program would transfer all historical information collected by paper-format business registrars into electronic format and facilitate its access via the internet or similar media (known as e-distribution). It would also implement an electronic system where a business could register its company over the internet – regardless of the necessary amount of legal physical paperwork – creating a “digital signature,” thereby speeding up the entire process (known as e-registration). Currently, less than 35% of the countries outside the OECD have achieved this goal, yet many others are developing or implementing the necessary technological and legal infrastructure (i.e. electronic signature laws) to do so. For instance, the European Union _First Initiative_ requires all EU countries to digitalize all their public registrars (see Box 3).

# **Box 3: Supranational registries: The BRITE Project**

With the goal of establishing a dynamic business ecosystem, the European Union, in conjunction with the European Business Registry, has launched the “Business Register Interoperability Throughout Europe” (BRITE) project in 2006. BRITE aims to address the cross border registration problem in the EU by creating a common and unified European Business Registry. The main challenge is the interoperability of all EU registries through the implementation of a common electronic platform in 18 different countries. The objective of BRITE is to harmonize the collection and distribution of business registry data, in order to facilitate greater private and public sector access to corporate data. The goal is also to promote registry data as part of greater e-government initiatives.

11
size is then successively increased by one observation at a time until the end of the sample.

The corresponding trace-statistics are plotted in Figure 2. The graph is scaled such that unity corresponds to the 10% level of significance. As can be seen from Figure 2, the trace-test statistic rejects the hypothesis that the rank is null only from the late 1980s onwards. Thus, only from approximately 1990 does there seem to be a common stochastic trend between the three sectors. Prior to that there appears to be no evidence for cointegration between the three sectors. To investigate this apparent break in the sample further, we split the sample into the period prior to and post 1990 and perform two separate cointegration analyses.

**Figure 2:** Recursive trace test for cointegration between agricultural, industrial and service sector GDP 1965 - 1998)

<!-- Start of picture text --> The Trace tets<br>1 4~~~~~~~~~~~~~~<br>1 2<br>10 _ _ _ _ _0 __ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ __'''<br>09<br>007<br><!-- End of picture text -->

# **4.2 The sample from 1965:01 to 1989:04**

When estimating a VAR from 1965:04 to 1989:04, non-normality is strongly rejected for the industry GDP series. An analysis of the residuals indicates that the non-normality is due to two large jumps in the series. These may be caused by the rapid increase in industrial output after the foundation of a national oil industry in 1972 and by the aftermath of a major earthquake in March 1987, which destroyed the export oil pipeline and halted oil exports for 5 months. The rapid increase of industrial output after 1972 as well as the 1987 earthquake represent large outliers and could bias the outcome of our 10
Gopalakrishna, S., G. Lilien, J. Williams, and I. Sequeira (1995), “Do Trade Shows Pay Off?” Journal of Marketing, Vol. 59(3), pp. 75-83.

Hausmann, R. and D. Rodrik (2003), “Economic Development as Self-Discovery”, Journal of Development Economics, Vol. 72(2), pp. 603-633.

Kweka, J., O. Morrissey, and A. Blake (2003), “The Economic Potential of Tourism in Tanzania”, Journal of International Development, Vol. 15, pp. 335-351.

Lejarraga, I. & P. Walkenhorst (2013), “Economic Policy, Tourism Trade and Productive Diversification”, Working Papers 2013-07, CEPII Research Center.

Rauch and Watson (2001), “Entrepreneurship in International Trade”, NBER Working Paper 8708, National Bureau of Economic Research, Cambridge, MA.

# **Appendix**

**Table 8: Robustness Check – Inclusion of Crisis Dummy**

|**Vars**|**Exports**|**Exports**|**Exports**|**Exports**<br>**Outside**<br>**India**|**Exports to**<br>**India**|
|---|---|---|---|---|---|
|Lagged Log Tourists||-0.832|-0.322|-1.291|-0.0503|
|||(0.536)|(0.516)|(2.018)|(0.75)|
|Lagged Log Tourist*Touristic Export||||||
|Products||2.414***|1.720***|3.492**|-0.76|
|||(0.512)|(0.399)|(1.642)|(0.775)|
|Lagged Log Expenditure per day Tourists|||-0.113|2.726*|-0.00661|
||||(0.569)|(1.307)|(0.531)|
|Lagged Log Expenditure per day||||||
|Tourists*Touristic Export Products|||1.149**|-0.527|-0.786|
||||(0.495)|(0.99)|(0.679)|
|Log World Imports of the Same Product|1.174***|0.963***|0.724***|0.664**|0.629|
||(0.107)|(0.168)|(0.193)|(0.241)|(0.397)|
|Log Real Exchange Rate|-0.854***|-0.731***|-0.682***|-0.883***|-0.425*|
||(0.159)|(0.184)|(0.178)|(0.251)|(0.239)|
|Crisis|-0.0303|0.0452|0.0846|0.0489|0.413|
||(0.314)|(0.31)|(0.282)|(0.411)|(0.291)|
|Constant|-10.60***|-23.23***|-16.10*|-19|1.148|
||(1.255)|(6.713)|(9.199)|(13.88)|(6.979)|
|Observations|362|350|336|305|147|

21
15 of the quality of the legal system and a negative effect of bureaucratic costs on the level of use of host economy suppliers. Related to this, Perez-Villar and Seric (2015a) and Amendolagine et al. (2019) find that the level of institutional proximity between home and host economies exercises a positive effect on the extent of backward linkages. Using firm-level data for 28 transition economies, Monastiriotis (2016) estimates productivity spillovers from MNC affiliates from different home economies and finds that positive effects materialize only from affiliates originating in European Union (EU) member countries, indicating that institutional proximity fosters productivity spillovers.

_Adequate financial markets_ . The presence of financial markets that function well is also important. Javorcik and Spatareanu (2009) find for a sample of domestic firms in the Czech Republic that, whereas in general domestic firms experience financial constraints, domestic firms that operate as supplier to MNC affiliates do not. Furthermore, MNC affiliates that provide support to improve their suppliers expect domestic firms to make investments themselves to enhance the impact of supportive linkages (Alfaro-Urano, Manelici, and Vasquez 2019; Jordaan 2011b, 2013). To be able to make such investments, which can facilitate the effects of technology dissemination, domestic firms need access to finance.

_Trade policy_ . Several studies present findings that _trade policy_ generates positive productivity effects. For instance, Farole and Winkler (2014) report an estimated positive effect of an interaction variable between a host economy’s level of trade and the presence of MNC affiliates. For China, Du, Harrison, and Jefferson (2014) report a significant positive effect of an interaction variable between the level of inter-industry MNC participation and a dummy variable capturing the year that China joined the World Trade Organization (WTO). They also find a negative effect of an interaction variable between inter-industry MNC and tariff levels. These effects of trade openness are likely to capture the presence of pecuniary externalities. Access to international markets makes it easier for MNC affiliates to source inputs from international markets, placing local suppliers in direct competition with international suppliers. The estimated positive effects of trade openness suggest that domestic suppliers respond to this change in market conditions by improving their performance, which takes the form of positive productivity effects.

# **3.3.2 Mediating factors**

_Global value chains._ GVCs constitute an important factor promoting linkages between domestic firms and MNC production networks (UNCTAD 2013). GVC participation is considered to be an important mode of export-based development, given that domestic firms that join GVCs obtain access to international markets (Taglioni and Winkler 2016, World Bank 2020), creating opportunities to learn
_5_

# **H1. Stock Market Development Indicators**

This section presents and discusses an array of stock market development indicators. Specifically, we describe measures of (a) market size, (b) market liquidity, (c) market volatility, (d) market concentration. (e) asset pricing efficiency. (f) regulatory and institutional development, and (g) conglomerate indexes that aggregate the information contained in measures (a)-(f). We use data from the International Finance Corporation's _Emerging Markets Data Base_ and the _International Financial Statistics_ of the International Monetarn Fund. The data cover the 1986-1993 period for 41 countries. Table I lists the names of the countries.

# _A. Stock Market Size_

The market capitalization ratio equals the value of listed shares divided by GDP and analysts frequently use the ratio as a measure of stock market size. In the rest of the paper, we refer to this measure as "market capitalization." In terms of economic significance, the assumption behind market capitalization is that market size is positively correlated with the ability to mobilize capital and diversify risk. As indicated in Table 1, South Africa, Hong Kong, Malaysia, Japan, and Singapore all had market capitalization ratios of greater than one over the 1986-1993 period, while Nigeria, Argentina, Indonesia, Colombia, and Turkey all had market capitalization ratios of less than 0.1 over the same period.

We also include statistics on the number of listed companies as an additional measure of market size. While marginal differences in the number of listed companies are uninformative, extreme values can be useful. Specifically, it is not very interesting that Australia averaged 1,184 listed companies and Canada averaged 1,118 listed companies over the 1986-1993 period.
WPS3800

# **Is Formal Lifelong Learning a Profitable Investment for All of Life?**

## **How age, education level, and flexibility of provision affect rates of return to adult**

## **education in Colombia**

Thomas Pave Sohnesen and Andreas Blom World Bank

## JEL: I2; J24

## **Abstract**<sup>**1**</sup>

_Lifelong learning is increasingly being recognized as a primary factor for knowledge diffusion and productivity growth. However, little economic evidence exists on the economic value of lifelong learning for the individual, especially in developing countries. This paper contributes to remedy this shortfall. It investigates one aspect of lifelong learning: returns to formal education across ages. In the absence of long-term longitudinal data, the paper estimates rates of return for simulated re-entry into the education system. The estimations use the method of internal rate of return and are based on observed education-age-earnings profiles from the Colombian national household survey. We find that rates of return to all levels of education are only slightly smaller for 35 year olds than for young people, thus confirming the profitability of investment in adult education. Tertiary education continues to attract a positive return until late in life, 45-50 years, whereas the economic value of reentering primary and secondary education is positive up till the age of 40-45. Thus, formal lifelong learning seems to remain a profitable investment for at least half of life. However, lack of part-time work, high tuition fees, and prolonged study time reduce the return. The findings suggest that adult formal education initiatives should focus on the 20 to 40- year olds and be designed flexibly in order to allow learners to work part time._

## World Bank Policy Research Working Paper 3800, December 2005

_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://econ.worldbank.org._ 1 The authors thank colleagues from the World Bank: Eduardo Velez Bustillo, Kristian Thorn, Joshua Gallu, Harry Patrinos, Lauritz Holm-Nielsen, and Sergei S. Soares as well as participants at a World Bank Seminar on Lifelong Learning in Latin America and the Caribbean in August 2003 for their valuable comments. Email of corresponding author: ablom@worldbank.org.

1
increases small farm income by 58% (+$3.2 billion), again largely because of an increase in the stock. By contrast, a warming of 2.5 ° C reduces large farm livestock income by 22% (-$13 billion) and a warming of 5 ° C reduces it by 35% (-$20 billion). This reduction for large farms is due to both a shrinking of the stock and a reduction in net revenue per animal owned. Increased precipitation of 14% reduces small farm income by 10% (-$0.6 billion) mostly due to a shrinking of the stock. The same precipitation reduction reduces large farm income by 9% (-$5 billion) due to a reduction in both the stock and the net revenue per animal owned.

The report also tested the impact of climate scenarios predicted by three Atmospheric Oceanic General Circulation Models (AOGCMs). With the relatively hot scenario predicted by the Canadian Climate Center (CCC) model in 2100, small farms would increase net revenues by 116% (+$6 billion). For the large farms, CCC leads to a 23% loss (-$14 billion) by 2100. The 2100 results for small farms under the Center for Climate System Research (CCSR) climate scenario is an increase in net income by 152% (+$8 billion). For large farms, CCSR leads to losses of a negligible amount. The Parallel Climate Model (PCM) climate scenario for 2100 leads to an increase of net income by 31% (+ $2 billion) for small farms and a decrease by 27% (-$16 billion) for large farms. In general, scenarios with low rainfall predict higher net revenues.

The analysis finds that warming is good for small farms because they can substitute animals that are heat tolerant. Large farms, by contrast, are more dependent on species such as cattle, which are not heat tolerant. The wetter scenarios are likely to be harmful to grazing animals because greater rainfall implies a shift from grasslands to forests, an increase in harmful disease vectors, and a shift from livestock to crops.

The results indicate that livestock on large farms are vulnerable to climate change and are likely to lose net revenue unless there is substantial drying. Small farms are much less vulnerable and will probably increase net revenue from climate change. Overall, because large farms dominate the sector, African livestock net revenues are expected to fall. However, if future climates turn out to be dry, livestock net revenue will increase. At least against the risk of dryness, livestock offer a good substitute for crops.

3