rafmacalaba/datause-encoder-data huggingface dataset:
**Figure 3: Regression coefficient for trade openness, and the 95 percent confidence interval**

<!-- Start of picture text --> A B C D E<br>Source: Authors’ estimates using data from World Development Indicators and povcalnet.<br>Note: The y-axis measures the regression coefficient for trade openness, and the brackets show the 95 percent<br>confidence interval. When these confidence intervals cross zero, the correlation is not statistically significant.<br>.5<br>0<br>-.5<br>Regression coefficient [95% C.I.]<br>Poverty rate Poverty rate (fe) Poverty rate (fe, yr) B40 income gr B40 income (fe, yr)<br><!-- End of picture text --> This is because in the short run, a trade-related shock results in simultaneous responses by consumers and producers. Changes in trade policies or global trade patterns affect the prices of goods and services for domestic consumers and producers. The change in relative prices in turn affects labor demand, potentially increasing jobs or wages in some sectors and downsizing in others, thus affecting the earnings of producers and employees. For example, reducing a trade barrier on a good can reduce its domestic price and increase competition from international producers, causing domestic production to contract. Or an improvement in trade logistics can increase trade by reducing domestic prices that increase the international competitiveness of domestic production, fostering increased demand for exports. The net effect of a trade-related price shock and how it is transmitted to households will depend on whether or not the affected goods are consumables or inputs to domestic production or to exportables.

The short-run adjustments by producers and consumers to a trade-related shock will determine who benefits and who loses in the short run. This paper focuses on the short-run effects of trade on households in order to capture the potential welfare impact on the poor, guided by the Bank’s twin goals. In addition to welfare and equity considerations, identifying potential winners and losers is important for the political economy of the trade reforms, given that potential “losers” tend to act as stumbling blocks to reform, which can result in inefficient trade policies (Baldwin and Robert-Nicoud 2007).

# **3. Trade shocks to welfare: Transmission channels to consumers and producers**

The trade patterns associated with an economy’s production structure, income levels and consumption habits determine which economic agents are linked to global markets and how they are affected by trade shocks. For a price increase in consumables, for example, households that consume these final goods will incur a higher cost and may reduce their consumption. If, on the other hand, households are engaged in the production of the consumable, the price increase would translate into increased earnings. But if the same product was an input into production, the higher 5
Figure 9: Correlation between Innovation score and IO Coefficient of ICT‐services (2013), Vietnam <!-- Start of picture text --> Innovation Score and IO Coefficient<br>Chemicals & Pharm.<br>Electrical equipmentMotor vehiclesMachinery and equip.<br>Computer & Electr.<br>Non-metallic mineralOther transport<br>Food & BevBasic m e tr a lsges Rubber & Plastics<br>Metal products<br>Textiles & WearingOther manuf. & Repair Paper & Printing<br>Wood products<br>.0005 .001 .0015 .002 .0025<br>IO Coefficients (Domestic)<br>.1<br>.08<br>.06<br>Innovation Score<br>.04<br>.02<br><!-- End of picture text --> Source: World Bank. Note: the IO Coefficients are computed as the fraction of ICT‐services usage in total input use for each industry in Vietnam using IO tables. Innovation variable in this figure represents a composite indicator measuring (i) whether the firm undertakes R&D; (ii) whether R&D is new to the market or world; (iii) whether the firm has national and international patents; (iv) whether the firm is involved in any research collaborations. The fitted values line is plotted on the basis of excluding Paper & Printing. Coke & Petroleum is omitted because of lack of credible data.

Figure 10: Vietnam’s level of data policy restrictiveness (2010‐2013) <!-- Start of picture text --> Level of data innovation restrictiveness<br>2010 2011 2012 2013<br>.8<br>.6<br>.4<br>Data innovation restrictiveness .2<br>0<br><!-- End of picture text --> Source: ECIPE 27
# **Appendix B: Plots of precipitation anomalies**

# _<u>Coastal stations</u>_

<!-- Start of picture text --> CAMPO DE MARTE CHICLAYO<br>6 450<br>5 400<br>4 350<br>3 300<br>2 250<br>1 | 200<br>0 150<br>-1 100<br>-2 50<br>-3 0<br>-4 -50<br>may-48 oct-53 abr-59 oct-64 mar-70 sep-75 mar-81 ago-86 feb-92 ago-97 feb-03 may-48 oct-53 abr-59 oct-64 mar-70 sep-75 mar-81 ago-86 feb-92 ago-97 feb-03<br>LIMA CALLAO/ APTO. INT. JORGE CHAVEZ SAN JUAN<br>500 300<br>250<br>400<br>200<br>300<br>150<br>200<br>100<br>100<br>50<br>0<br>0<br>-100 -50<br>may-48 oct-53 abr-59 oct-64 mar-70 sep-75 mar-81 ago-86 feb-92 ago-97 feb-03 may-48 oct-53 abr-59 oct-64 mar-70 sep-75 mar-81 ago-86 feb-92 ago-97 feb-03<br>PISCO PIURA<br>250 800<br>700<br>200<br>600<br>150 500<br>400<br>100<br>300<br>200<br>50<br>100<br>0<br>0<br>-100<br>-50<br>may-48 oct-53 abr-59 oct-64 mar-70 sep-75 mar-81 ago-86 feb-92 ago-97 feb-03<br>may-48 oct-53 abr-59 oct-64 mar-70 sep-75 mar-81 ago-86 feb-92 ago-97 feb-03<br>Precipitation Anomalies compared to average for 1960-1990 (Milimeters) Precipitation Anomalies compared to average for 1960-1990 (Milimeters)<br>average for 1960-1990 (Milimeters) average for 1960-1990 (Milimeters)<br>Precipitation Anomalies compared to Precipitation Anomalies compared to<br>average for 1960-1990 (Milimeters)<br>average for 1960-1990 (Milimeters) Precipitation Anomalies compared to<br>Precipitation Anomalies compared to<br><!-- End of picture text -->

31
median voter, which imply a negative correlation between democracy and centralization. We use _Democratic Environment_ and _Quality of Governance_ as two types of institutional variables. These come from the International Country Risk Guide (ICRG) published by the PRS Group.<sup>12</sup> _Democratic Environment_ is the average of the following political risk components law and order, democratic accountability, military in politics, and religion in politics. _Quality of Governance_ is the average of law and order, bureaucratic quality and corruption.<sup>13</sup> Finally, we add two other variables that were not specifically used by the literature mentioned above. First, we have _Trade Openness_ , traditionally defined as the ratio of the sum of exports and imports to GDP. Rodrik (1998) argues that more open countries have larger governments to stabilize the economy against external shocks. To the extent that the stabilization role is undertaken by the central government we would expect a positive relationship between openness and centralization. The second variable we introduce is _External Conflict_ . This may be an important factor in centralization especially in the MENA region. Tosun and Sen (2007) show that the MENA countries have more major conflicts such as war, on average, than other developing countries. They find evidence of significant spatial spillovers from regional conflicts. Alesina and Spolaore (2003) present a model with conflict and size of nations. They show that external shocks give a strong incentive to form larger jurisdictions. We draw a parallel and further argue that external shocks may legitimize greater central government budgets since national defense is a function of government undertaken by the central government. Alesina and Spolaore also address the interaction between democratization and conflicts,

> 12 Note that higher risk points correspond to an improvement in the institutional variable.

> 13 This was recently used as a measure of governance quality by Knack (2001).  Components of these institutional variables, particularly corruption in government, were used in many other studies including Tanzi and Davoodi (2000), Mauro (1996) and Knack and Keefer (1995).

16
# **Appendix Table A2: Estimates of Ω, the CET Elasticities**

|**Country**||**Estimat**|**e and test**|||||**Model**|
|---|---|---|---|---|---|---|---|---|
||**Ω**|**Standard**<br>**Error**|**t-**<br>**statistic**|**NOB**<br>**afte**<br>**r adj**|**Method**|**Lag**<br>**interval**<br>**in VEC**|**C and trend**<br>**structure in VEC**|**Additional specifications**|
|Yemen|0.669|0.243|2.756|25|VEC|1,3||factor demand (re-export) spec; dummy before 1994 and<br>dummy after 2005 exogenous in CE and VAR; xgr<br>exogenous in VAR|
|Zambia|0.232|0.063|3.709|47|GMM|||C, T, ar(1), ma(1) as extra vars; insts=xgr, eshare, global<br>demand ratio|
|Zimbabwe|0.462|0.163|2.831|46|VEC|1,2|C, T in CE; C in VAR|global demand and price ratios exogenous in VAR|

**Source:** Authors' calculations **Notes:**

* = low t-test, insignificant at prob=0.05.

C = constant CE = cointegration equation dummy = dummy variable egr = growth rate of real exports eshare = exports/GDP FMOLS = fully modified OLS for cointegration regression global demand and price ratios = variables in equation 9 GMM = generalized method of moments inst(s) = instrument variable(s) LIML = limited information maximum likelihood lyx = log of output (real GDP) index mgr = growth rate of real imports mshare = imports/GDP NOB = number of observations after adjustments T = linear trend T^2 = quadratic trend reduced eq of export system = equation 9 VEC = vector error correction VAR = error correction part in VEC or vector autoregression xgr = growth of output (real GDP) 37
consistent with the hypothesis earlier – firms in TEs will do this more frequently as transition progresses and they catch up to the developed market economies. Again, we see a pattern of catching up and convergence. The gap is smaller or nonexistent with respect to upgrading. Interestingly, in 2005 there is virtually no variation across countries in terms of discontinuation of products: 15-17% of firms in Germany, the cohesion EU countries, and all the transition countries, report that they had discontinued a product in previous years. The exception is East Germany, which reports considerably lower levels of restructuring activity than observed in firms in West Germany or anywhere else.

The second pattern in the data is more of a surprise. The raw data show an inverse U- shape pattern of restructuring in the transition countries between 1999 and 2005: restructuring activity is low in 1999, high in 2002, and lower again in 2005. The other measures of restructuring available in the BEEPS surveys produce similar results (with the sole exception of “obtained a new quality accreditation”). To check the robustness of this finding, we estimated a restructuring activity equation (see below) on the full set of three years of data, including all the controls that we use below (firm size, sector, ownership, etc.), in order to remove any effects of changes in the composition of the BEEPS survey samples. The U-shape pattern appears strongly in these regression results (Table VI.2), across all the measures of restructuring we use, including the measure that captures both defensive as well as deep restructuring: discontinuation of product lines in the TEs peaks in 2002. The U-shape pattern extends across transition country regions as well.

This pattern is puzzling. A U-shape pattern is consistent with the literature on restructuring discussed earlier: if the early transition period is dominated by defensive restructuring, the opposite side of the coin is that deep restructuring starts with a lag and then accelerates. But why should the timing of the peak of restructuring activity in the new EU member states coincides with the one in middle income and low income CIS countries? It is widely believed that the more developed transition countries were faster reformers at the start of the transition period, and, hence, more restructuring activity in 38
# _Circuit_ theory of finance and the role of incentives in financial sector reform

## by

Biagio Bossone

## World Bank

## Summary

This paper analyzes the role of the financial system for economic growth and stability, and addresses a number of core policy issues for financial sector reforms in emerging economies. The role of finance is studied in the context of a _circuit_ model with interacting rational, forwardlooking, and heterogeneous agents. Finance is shown to essentially complement the price system in coordinating decentralized intertemporal resource allocation choices from agents operating under limited information and incomplete trust. The paper also discusses the links between finance and incentives to efficiency and stability in a circuit context. It assesses the implications for financial sector reform policies and identifies incentives and incentive-compatible institutions for financial sector reform strategies in emerging economies.

The author is intellectually indebted to the work of Prof. Augusto Graziani in the field of monetary circuit theory. The author wishes to thank Jerry Caprio, Stjin Claessens, and Larry Promisel for their helpful comments on earlier drafts of the paper. He bears full responsibility for any remaining errors and for the opinions expressed in the text. The author is especially grateful to his wife Ornella for her invaluable support. For comments, contact Biagio Bossone, E-mail: Bbossonetworldbank.org, tel. (202) 473-3021, fax (202) 522-2031.
Policy Research Working Paper 0

# **Abstract**

This paper considers the question of whether international banks learn from their previous crisis experiences and reduce their lending to developing countries in the event of a financial crisis. The analysis combines a bank-level dataset of bank activity and ownership with country-level data on the stock of historical crisis events between 1800 and 2005. To circumvent selection and endogeneity concerns, the paper exploits temporal variations in the relative recency of crises as instruments for crisis experience. The results indicate that foreign banks with greater crisis experience reduced their lending significantly more relative to other foreign banks, which can be interpreted as evidence in favor of a learning effect. The findings survive robustness checks that include alternative measures of crisis experience, additional controls, and decompositions into different types of crises. The question of learning is also examined from the perspective of other measures of bank performance.

This paper is a product of the Global Modeling and Analytics Unit, Development Prospects Group. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The authors may be contacted at gminne@ulb.ac.be and jlim@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
Table 1: Demographic Differences By Baseline Overdrafter Status

||(1)|(2)|(3)|
|---|---|---|---|
||Light|Heavy-||
||Overdrafter<br>Mean|Light<br>Diference|N|
|Fraction Months in Overdraft: Past Year|0.0578|0.481<sup>_∗∗∗_</sup>|318|
||[0.141]|(0.0242)||
|Female|0.3|-.129<sup>_∗∗∗_</sup>|318|
||[0.46]|(0.0472)||
|Age|45.5|4.53<sup>_∗∗∗_</sup>|318|
||[9.63]|(0.945)||
|Teacher|0.469|0.202<sup>_∗∗∗_</sup>|318|
||[0.501]|(0.0545)||
|Other Govt. Employee|0.35|-.0272|318|
||[0.478]|(0.0531)||
|Formal Private Sector Worker|0.188|-.181<sup>_∗∗∗_</sup>|318|
||[0.392]|(0.0316)||
|Education: Secondary Graduate|0.344|-.0526|318|
||[0.476]|(0.0523)||
|Education: Postsecondary|0.606|0.0583|318|
||[0.49]|(0.054)||
|Married|0.85|0.0487|318|
||[0.358]|(0.0372)||
|Biological Children Ever Born|3.14|1.32<sup>_∗∗∗_</sup>|318|
||[2.1]|(0.252)||
|Householders 14 and Under|2.15|-.0677|318|
||[1.73]|(0.19)||
|Householders 15 and Older|3.5|0.69<sup>_∗∗∗_</sup>|318|
||[2.04]|(0.237)||
|Income Last Month|729|15.4|302|
||[372]|(45.3)||
|Savings - NVRB (Reported)|324|-186<sup>_∗∗∗_</sup>|283|
||[612]|(58.9)||
|Savings - NVRB (Adminstrative)|356|-243<sup>_∗∗∗_</sup>|318|
||[699]|(63.2)||
|Total Cash Savings|1672|-399|249|
||[3249]|(358)||
|Total Debt|1930|1252<sup>_∗∗∗_</sup>|308|
||[2428]|(329)||
|Economic Shock|0.481|0.0377|318|
||[0.501]|(0.0562)||
|Financial Strain: Missed School|0.313|-.00237|318|
||[0.465]|(0.0521)||
|Financial Strain: Pay Bills|0.675|0.116<sup>_∗∗_</sup>|318|
||[0.47]|(0.0493)||
|Financial Strain: Pay Debt|0.563|0.0957<sup>_∗_</sup>|318|
||[0.498]|(0.0546)||
|Socially Taxed|0.619|-.0301|318|
||[0.487]|(0.055)||
|Any Food Insecurity|0.444|0.0562|318|
||[0.498]|(0.0561)||

Notes: Standard deviations in brackets, heteroskedasticity robust standard errors in parentheses. All variables denominated in Ghanaian Cedis top-coded at the 99th percentile. In 2013 GHS 2.15 _≈_ USD 1. * p _≤_ 0 _._ 10, ** p _≤_ 0 _._ 05, *** p _≤_ 0 _._ 10.

17
# **Infrastructure Gap in South Asia: Infrastructure Needs, Prioritization, and Financing**<sup>**1**</sup>

**Luis Andrés, Dan Biller, and Matías Herrera Dappe**

Keywords: Infrastructure needs, Prioritization, Financing, South Asia.

JEL Classication: D6, O1, O18, O53, R10 1 The South Asia Infrastructure Needs Regional Study is the product of a team effort of the Economics Team in the Sustainable Development Department and the Chief Economist Office in the South Asia Region of the World Bank. The team would like to thank Jorge J. Escurra, Atsushi Iimi, Fernanda Ruiz-Núñez, and Stefanie Sieber for preparing the sectoral models, as well as Diana Cubas, Céline Ferré, and Ada Karina Izaguirre for contributing to the background papers. The team would also like to acknowledge the support from Gonzalo Vázquez Baré, Cecilia Belita, Neetu Mihal, Diana Moreira, and Shaheena Nisar. Finally, the team would also like to thank Edgar C. Bouharb for his Information Technology support. The team also thanks Ashma Basnyat, Juan Echenique, Matías Herrera Dappe, Rahul Kanakia, Mario Picon, Govinda Timilsina, and Laura Wallace for directly contributing to the final product. The team is grateful for the ongoing support and technical inputs from the South Asia Regional Chief Economist, Martin Rama and the former regional Chief Economist, Kalpana Kochhar. The team also greatly appreciates the guidance from Jack Stein, Gajan Pathmanathan, Jyoti Shukla, the South Asia Sustainable Development Department management team, and Country Management Units in the SAR region. The team appreciates the feedback and inputs of the South Asia regional team. In particular, the team would like to acknowledge Sudeshna Banerjee, Kirsten Hommann, Johannes G. P. Jansen, Pravin Karki, Bill Kingdom, John Newman, Pradeep Mitra, Mohua Mukherjee, Elisa Muzzini, Sheoli Pargal, Rabin Shrestha, and Tomoyuki Yamashita, for their valuable inputs during the preparation of this report. In addition, Marianne Fay (SDN, Chief Economist), Vivien Foster (SEGEN, Sector Manager), and José L. Guasch (Consultant) have all provided insightful and constructive comments through their capacities as peer reviewers. Financial support from SAR Chief Economist Office and the Australian Government is gratefully acknowledged.
_Gender Equality and Economic Development: The Role for ICTs_ 22 in education onto gender equality in employment may thus provide a second indirect channel via which ICT availability is able to improve gender equality in employment<sup>15</sup> .

# **8. Concluding Remarks**

Using OLS and IV panel regressions with country-fixed effects, this paper empirically assesses the effects of the level of information and communication technologies (ICT) infrastructure on gender inequality in education as well as in employment. Five indicators are used to proxy for the level of ICT infrastructure. They are the number of computers per 1,000 persons, the number of Internet users per 1,000 persons, the number of phones (both mainlines and mobile) per 1,000 persons, ICT expenditures as a share of GDP and ICT expenditures per capita. As for the gender inequality measures, the ratio of female to male students in primary and secondary schools is used as the measure for gender inequality in schools, while the female to male ratio of labor force activity rates acts as the proxy for gender inequality in the labor market.

The results indicate that the level of ICT infrastructure exerts a statistically significant positive effect on gender equality in education. This suggests that improvements in the level of ICT infrastructure lead to improvements in gender equality in education. It was also found that, to some extent, the level of education among the general population is important for improvement of gender equality in schooling.

The employment gender equality panel regressions reveal that the level of ICT infrastructure has a statistically positive effect on gender equality in employment. This implies that improvements in the level of ICT density tend to improve gender equality in the labor market as measured by the female to male ratio of the labor force activity rates. One possible reason for this positive effect is that the availability of ICTs enables women to telecommute and work from home. Another is that ICT use increases the flow of information and knowledge, and thus facilitates the changing of public opinion to accept the idea of women working.

It was also found that the prevailing level of education among the population has a statistically significant positive effect on gender equality in the labor market. It is postulated that education in general tends to broaden one’s awareness of cultures and social norms that exist in other societies, and that in many of these societies, especially in industrial economies, females are in most circumstances entitled to the same freedoms and opportunities as extended to males. In addition, there was some evidence suggesting 15 Recall that ICT availability is also able to improve gender equality in education.