rafmacalaba/datause-encoder-data huggingface dataset:
It is still an open question as to what are the largest contributing factors to the increase in observed impacts. Studies addressing this question often involve normalizing economic losses to adjust for inflation, changes in wealth and population changes (Neumayer and Barthel 2010; Crompton and McAneney 2008). As a result, trends in losses may be wholly attributed to inflation, wealth and population changes as no further trends in losses are observed after normalization. Clearly these drivers related to exposure are highly influential in the past growth of flood impacts.

However, these studies do not prove conclusively that hazard is not increasing or that vulnerability is not important, as the possible impact of mitigation is not taken into account during normalization. Authorities and individuals have taken steps towards minimization of losses. Increased frequency could be masked by their success (Neumayer and Barthel 2010). Furthermore, the balance of drivers in the future may differ from the past. Therefore it is impossible to say with any certainty which drivers are the most important. Accordingly, the causes of increased impacts are categorized below in a structured way to illustrate that all underlying causes are considered in the process of designing strategies to limit flood damages in the future.

# **2.1 Causes of increased urban flood hazard**

**The sources of urban flooding are similar to the general flood scenario as urban settlements are subject to the same natural forces as rural areas such as rainfall, snowmelt, sea surge and failure of water systems.** The pathways by which these sources of flooding threaten urban settlements include rivers and overland flows. An additional major pathway for urban flooding is intense rainfall overwhelming the drainage holding capacity of a city. More importantly, within the urban environment it is not only increases in the sources of flood water which increase flood hazard. Other anthropogenic factors, which have an impact on the conveyance of water towards urban areas and which decrease the ability of urban spaces to disperse water, will also tend to increase the flood hazard within the urban environment and are considered in this section.

# **2.1.1 Climate change impact**

# **In a recent report the World Meteorological Organization (WMO) summarizes some**

**of the climate anomalies that have been observed recently.** For instance the 10 warmest years on record have all occurred since 1998. At the end of the melt season in September 2010, the ice extent in the Arctic Sea was the third smallest on the satellite record after 2007 and 2009. Global

17
WPS3455

# **LABOR MARKET DISTORTIONS, RURAL-URBAN INEQUALITY AND THE OPENING OF CHINA’S ECONOMY**<sup>*****</sup>

Thomas Hertel Center for Global Trade Analysis, Purdue University Fan Zhai<sup>**</sup> Asian Development Bank Keywords: Labor market, Income distribution, WTO, Computable general equilibrium, China JEL Classification Code: C68; J60; D30; F13; O53

## World Bank Policy Research Working Paper 3455, November 2004

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the view of the World Bank, its Executive Directors, or the countries they represent. Policy Research Working Papers are available online at http://econ.worldbank.org._

> * Senior authorship is not assigned. This work was undertaken with partial support from the join Development Research Council/World Bank project on _“China’s WTO Accession and Poverty.”_

> ** Corresponding author. Address: Asian Development Bank, PO Box 789, 0980 Manila, Philippines.  Tel: 63-26325956; Fax: 63-2-6362360; Email: fzhai@adb.org
10 STCs are more likely to indicate the presence of a discriminatory measure, and have the additional benefit of having been coded by the WTO Secretariat and made available with the World Trade Report (2012). However, it is not obvious how representative these measures are of the entire spectrum of potentially discriminatory TBT and SPS measures, as governments may have a number of reasons to raise, or avoid raising, a trade concern vis-à-vis other partners.

The third source of information on NTMs focuses on business concerns. The International Trade Center (ITC) regularly conducts business surveys to identify non-tariff measures that create major concerns for exporters. In addition, certain countries compile information on trade barriers faced by their exporting firms, in some cases making this information publicly available.<sup>8</sup> As for specific trade concerns, business surveys are more likely to identify discriminatory NTMs and can often be quite detailed. However, surveys from different sources tend to be of heterogeneous quality and may suffer from sampling design problems. Moreover, they uniquely represent the view of firms and may be subject to a self-selection bias, as firms that respond are not necessarily a representative sample of the universe of exporters or potential exporters.

In sum, data on NTMs are highly incomplete and subject to measurement problems, particularly with regard to the proper identification of discriminatory measures. However, there are currently a number of initiatives to improve upon the existing situation. One such initiative is a multi-agency consortium on Transparency in Trade (TNT) launched by the African Development Bank, ITC, UNCTAD and the World Bank that, among other things, aims at refining and expanding data collection on NTMs and making new data available by 2016. Another initiative is through the Integrated Trade Intelligence Portal (I-TIP) of the WTO, which provides improved information on members’ notifications of NTMs and other sources of WTO-specific information on non-tariff measures. Yet another initiative is called PRONTO, which brings together a number of academic and policy institutions with the goal of improving data availability and methodologies to assess the trade impact of NTMs.

# **2.2.2 Some stylized facts on NTMs**

> 8 The ITC database on NTM business surveys is accessible at http://ntmsurvey.intracen.org/ntm-survey-data/. The European Commission’s database on trade barriers faced by exporters from the European Union is accessible at <u>http://madb.europa.eu/madb/indexPubli.htm.</u>
# 12. For examples of State and Commerce Departmnent warnings of the risks of German loans, see Eichengreen (1988).

13. Obviously, the greater precision of the estimate of the coefficient on budget balance in the equation for dollar bonds compared to its sterling counterpart may reflect the larger sample size.

# 14. This chronology follows Eichengreen and Portes (1987).

15. Central government debt to export ratios are not representative of the level of total debt. They are, however, the only debt indicators available for a wide range of countries, which is why we present them in Table 2. Where state and local government debts were substantial, the total government debt to export ratio could be much higher. Germany's for 1931 rises from 34 to 212 per cent, for example. Schulker (1988), p. 65.

# 16. Calculated from statistics in Harris (1935).

17. Lary (1943), p. 6 and Table Im after p. 216, estimates that debt service on dollar loans amounted to $900 million in 1929. The stock of U.S. portfolio investments abroad at the end of 1929 was on the order of $7.2 billion. Subtracting net short- and long-term lending in 1928 and 1929, which totalled $576 million (Lewis, 1938, pp.628-629) and multiplying the 1927/1929 ratio of debt stocks by $900 million yields $828 million.

18. Lewis (1949), p. 56.

# 19. Lewis (1949), p. _58._

20. Both figures are constructed from unweighted averages of country data. 'The heavy defaulters are Brazil, Bulgaria, Chile, Colombia, El Salvador, Germany, Greece, Guatemala, Hungary, Poland and Yugoslavia. The light defaulters are Argentina, Australia, Austria, Belgium, Czechoslovakia, Denmark, Finland, France, Italy, Japan, New Zealand, Nicaragua, Norway and Spain. Sources are described in the data appendix to Eichengreen and Portes (1986).

21. It is tempting to ascribe the 1934 imprevement in the commodity exporters' terms of trade to the 1933-34 devaluation of the dollar. Why the heavy defaulters should have benefitted more is not clear, however.

22. We use industrial production indices rather than GDP because their wider availability permits the inclusion of more countries. (We also suspect that the industrial production indices are more reliable.) Table 4 below documents that the choice of dependent variable is of little moment.

23. When the constant term is included, it differs from zero only because of rounding. The first equation of Table 3 becomes: IP = _-0.15_ - 9.70 Terms of Trade + 0.26 Default (1.08) (6.47) (0.04) with standard errors in parentheses.

51
**8**

# 1. ENERGY PRICE **INCREASES I[N** DEVELOPING COUNTRIES

# Background

Economic perfomance in developing countries during the last two decades can be divided into the decade of shocks (1970s) and the decade of adjustment (1980s). Extemal shocks affecting these countries were the commodity boom and the first oil price shock during the early 1970s, a rise in interest rates staring late in 1978 followed by a slowdowm in ihe world economy after 1978, a second oil price hike in 1978-80 and softening of oil and other primary commodity prices during the early 1980s. At the same time natural disasters and domestic policies created an urgent need for internal adjustment programs, which were implemented by country governments initially in collaboration with the IMF and later also with ihe World **Banl.** Producdon and distribudon of commercial energy, particularly electricity, in developing countries has often been reserved for the public sector. Diverse arguments based on efficiency and equity have been used to explain the highly centralized organization of ihe sector, including the presence of increasing returns in production and distrbution, the infiastructural role of the energy sector, and provision of energy to lowincome groups in the society, etc. Pricing of energy by the public sector has often been held below average supply costs. Although the need for increasing domestic energy prices was emphasized in adjustment policies' undertaken during the 1980s, the actual implementation was relatively slow, paricularly for electricity and kerosene.

Average taiff levels (in constant USS) for electricity in developing countries were relatively stable during the period 1979-83. During the next four years (1983-87) average electricity tarffs fell by around 35 percent, although there was no change in cost, thereby reaching a level of rouy half the average incremental costs of power system expansion for these countries.<sup>3</sup> Figure 1.1 shows the development in average electricity taiffs from 1980 to 1988 for developing countries and the OECD. Incremental supply costs for 1987 are also shown in the figure.

**'** World **Ban** (1990), **Su_may Data** Sheets of 1987 Power **and Commercial** Energy Statistics for **100 Deeloping Countri_,** **_Energy_ Seris Paper** No. **23, lindusty and Energy Depqtment** Woding Paper, Washington, D.C.

2 **Blal, B. (1981), Stucturl Adjustmnt Policies in Developing Economies, World Bak Staff Working Paper** No. 464, Mm World **Bank, Washington,** D.C.

> **2 Se.** World **Bak** (1990): Review of **Electricity** Tariffs **in** Developing Countries **During the 1980., Eergy Sera Paper** No. 32, **Industry mu nerg Dep_utnet, Wasington,** D.C.
where �′��� = �� + α� + ɛ′���

- ɛ′���: Idiosyncratic error, which varies across individual firms

Meanwhile, if a firm that would have invested in innovation decides not to under a certain threshold ( _z_ = 0), the observed innovation value ���� is censored around zero. To address this issue, I consider using an econometric technique for the censored dependent variable, such as Tobit. I will further discuss the potential censoring problem and solution in the next subsection.

# _Does innovation affect final output?_

To identify the effect of innovation on productivity, I adopt a production function approach that describes the relationship between factor inputs and output. Specifically, I use the standard Cobb-Douglas production function, where output is a function of the inputs the firm employs, such as labor, capital, and material, as well as its productivity. One advantage of using the production function is that it provides not only the coefficient for the variable of interest but also the coefficients of other factor inputs, which allow comparison of the relative size of the coefficient for variable of interest. Also, it reduces the problem of having to correct for other sources of firm heterogeneity that influence both innovation and overall performance, by including (controlling for) key factor inputs as repressors.

I consider innovation investment as a factor input and add the identified innovation variables in the production function. In this functional form, innovation is treated as an input, the same as labor, capital, and material. The residual is now unobservable, which takes out the effect of innovation inputs.

All variables are quantity terms (number of workers) or proxies for quantity terms that are measured by value terms divided by price index, as in previous literature.<sup>13</sup> For the LHS, I use gross output, measured by sales revenue deflated with the output deflators constructed from the INS’s Producer Price Index (PPI). Since there are no separate deflators for material

> 13 . Industry-level price indexes are usually applied to deflate firm-level sales and input expenditures in production function estimates.

21
# **References**

Acharya, V.V., I. Hasan, and A. Saunders. 2006. “Should Banks Be Diversified? Evidence from Individual Bank Loan Portfolios.” _Journal of Business_ 79 (3): 1355–1412.

Acharya, V. V., and S. Steffen. 2015. “The Greatest Carry Trade Ever? Understanding Eurozone Bank Risks.” _Journal of Financial Economics_ 115: 215–36.

Agarwal, I., R. Duttagupta, and A. F. Presbitero. 2019. “Commodity Prices and Bank Lending.” _Economic Inquiry_ 58 (2): 953–79.

Alesina, A., F. R. Campante, and G. Tabellini. 2008. “Why Is Fiscal Policy Often Procyclical?” _Journal of the European Economic Association_ 6 (5): 1006–36.

Armstrong, R, S. Morris, R. Smith. 2020. “Bank Investors Confront a New Fear: Oil Company Defaults” _Financial Times,_ March 9, 2020 Basel Committee on Banking Supervision. 2014. “Supervisory Framework for Measuring and Controlling Large Exposures.” Basel Committee on Banking Supervision, Basel, Switzerland. Beck, T., O. De Jonghe, and K. Mulier. 2017. “Bank Sectoral Concentration and (Systemic) Risk: Evidence from a Worldwide Sample of Banks.” CEPR Discussion Paper 12009, Center for Economic and Policy Research, Washington, DC.

Beck, T., and A. Demirguc-Kunt. 2006. “Small and Medium-Size Enterprises: Access to Finance as a Growth Constraint.” _Journal of Banking & Finance_ 30: 2931–43.

Berger, A. N., I. Hasan, and M. Zhou. 2010. “The Effects of Focus versus Diversification on Bank Performance: Evidence from Chinese Banks.” _Journal of Banking & Finance_ 34 (7): 1417–35.

Bidder, R. M., J. R. Krainer, and A. H. Shapiro. 2017. “De-Leveraging or De-Risking? How Banks Cope with Loss.” Working Paper 2017-03, Federal Reserve Board of San Francisco.

Blanchard, O., and J. Gali. 2010. “Labor Markets and Monetary Policy: A New Keynesian Model with Unemployment.” _American Economic Journal: Macroeconomics_ 2 (2): 1–30.

Boyd, J. H., and E. C. Prescott. 1986. “Financial Intermediary-Coalitions.” _Journal of Economic Theory_ 38 (2): 211–32.

20
results by analyzing firms’ performance in terms of partial indicators such as workers per connection, operating expenditures, and energy losses. Of course, the regulator is of limited use to the users if improvements on the supply side do not translate into improvements in the service received by the users. To track this we use three measures of social welfare: service coverage, frequency of interruptions, and residential tariffs.

The plan for the paper is as follows. Section 2 describes the dataset. Section 3 presents the econometric model. Section 4 shows the empirical results and provides evidence of their robustness. Finally, Section 5 concludes.

# **2. Data**

We exploit a unique dataset comprising firm-level information on 220 electric utilities from 51 development and transition countries for the period 1985 to 2005. The dataset includes the following variables: total electricity sold (in MWh), total number of connections in the utility area, total number of residential connections in the utility area, length of distribution network (in kilometres), total number of employees, operation expenditures (OPEX, in US dollars), average residential tariff (in US dollars), electricity losses due to technical and non-technical reasons (as a proportion of total electricity sold), frequency of interruptions (number per year), service coverage in the utility area, a dummy variable that takes the value of one if the firm is under the control of a regulatory agency (for more than six months), and a dummy a variable that takes the value of one if the firm received private participation (for more than six months). We define private participation as a situation where the private operator has control over the operation of the utility. We have also information on a set of country-level covariates including corruption, as measured by the Corruption Index produced by International Country Risk Guide (which ranges 4
Using this data, we estimate: where _αb_ again represents the matched-triplet fixed effects; _δt_ are week fixed effects; _s ∈{_ 1 _,_ 2 _,...,_ 160 _}_ indexes sections; and _t ∈{_ 1 _,_ 2 _,...,_ 34 _}_ indexes weeks. For panel models, we cluster our standard errors at the section-level, which in the Ebola sample coincides with the clinic, the level of randomization.<sup>23</sup> We amend Equation 3 to detect spillovers within our study sample — namely, the reallocation of patients from control to treated sections (or vice versa). Specifically, we interact our treatment indicators with covariates that, in the presence of such spillovers, should moderate our treatment effects (e.g., distance between sections, connections via roads).

# **4. Results**

## **4.1 Medium-term Outcomes**

We present the main medium-run survey results in Table 1, which shows estimated effects on clinic utilization, health outcomes, and patient satisfaction.<sup>24</sup> Our tables follow a common format. Column 1 provides the control mean and standard deviation, which are zero and one exactly when looking at family-level meaneffects indices. Column 2 presents the average treatment effect (in standard-deviation units) when pooling the treatment arms in a comparison with control. Columns 3 and 4 separately estimate the average treatment effects for CM and NFA, respectively. Column 5 shows the difference between the average treatment effect in CM and NFA. Column 6 provides the F-test for the joint null hypothesis of no effect from either treatment. Finally, Column 7 gives the sample size used for each regression.

Table 1 shows uniformly positive treatment effects across families for CM. Specifically, the general utilization of clinics increases by 0.11 standard deviation units when we pool the treatments, and the separate effects are similar for CM and NFA. Individuals in the control group recently used a western clinic for

> 23 When we collapse the data over time and estimate cross-sectional models, we omit the week fixed effects and _t_ subscripts. As treatment assignment occurs below the section-level, we do not cluster our standard errors in the cross-sectional models.

> 24 To gauge how individual indicators contribute to our family-level effects, Appendix Tables D.1–D.10 present treatment effects for each of the individual indicators. Tables D.11–D.19 repeat these analyses using the z-scored (i.e., control group-standardized) versions of the indicators. As discussed above, our primary approach to aggregating across individual indicators is to construct families of outcomes (i.e., mean-effects indices) per Kling et al. (2007). As a robustness check, we also use SUR to average treatment effects across indicators, and the results from this approach are presented in the top rows of the tables in Appendix Section D. Overall, our results are very similar using either method of aggregation; some coefficients attenuate under SUR, notably satisfaction and CDPE.

20
# Table A33: Indexes at short-term follow-up

|**_Loading factor_**|
|---|
|**Knowledge and awareness** _(Cronbach’s alpha = 0.31)_|
|Knows that father determines sex of child<br>0.455|
|Knows that 1/3 of women in the world experience violence<br>0.524|
|Thinks that VAW is an issue in India<br>0.579|
|Mentions either virginity ritual or menstruation rituals as prevalent gender norms<br>0.428|
|**Attitudes toward gender norms/roles** _(Cronbach’s alpha = 0.61)_|
|Thinks that is it wrong to follow the above-mentioned gender norms<br>0.352|
|Agreement (1-5) on “women should be virgin until marriage”<br>0.384|
|Agreement (1-5) on “a woman should be banned from entering the kitchen or household<br>shrine during her period”<br>0.385|
|Agreement (1-5) “women should be able to marry whomever they want, regardless of their<br>parents’ views”<br>0.072|
|Agreement (1-5) “girls should be allowed to wear whatever they want without being harassed”<br>0.190<br>Agreement (1-5) “parents should maintain stricter control over their daughters than their<br>sons”<br>0.454|
|Agreement (1-5) “when women get rights they are taking rights away from men”<br>0.345|
|Agreement (1-5) “it is more important that a boy goes to school than a girl”<br>0.389<br>Agreement (1-5) “nowadays men should participate in child rearing and household chores<br>ratherthanleavingitalltothewomen”<br>0.031|
|<br>Thinks that husband and wife should have the equal say in deciding how many children to<br>have<br>0.249|
|**Attitudes toward VAW** _(Cronbach’s alpha = 0.54)_|
|Agreement (1-5) on “a husband is justifed in hitting or beating his wife if she goes out<br>without telling him”<br>0.660|
|Agreement (1-5) on “a husband is justifed in hitting or beating his wife if he suspects her of<br>being unfaithful”<br>0.656|
|Would tell anyone if fnds out that a friend beats or physically hurts his partner<br>0.367|
|**Beliefs on others** _(Cronbach’s alpha = 0.79)_|
|Imagine to pick 10 of your closest Facebook friends. According to you, how many of them<br>think that women should be virgins till marriage?<br>0.484|
|Imagine to pick 10 of your closest Facebook friends. According to you, how many of them<br>think that men should be virgins till marriage?<br>0.504|
|Imagine to pick 10 of your closest Facebook friends. According to you, how many of them<br>think that a husband is justifed in hitting or beating his wife if he suspects her of being<br>unfaithful?<br>0.511|
|Imagine to pick 10 of your closest Facebook friends. According to you, how many of them<br>think that a husband is justifed in hitting or beating his wife if she goes out without telling<br>him?<br>0.501|

Notes: The _Global Index_ (Cronbach’s alpha = 0.71) is created using all variables reported above with the exception of those contained in the _Beliefs on others_ index. All variables were re-oriented so that the impact of treatments on each component of the index should be positive. The column on the right reports the loading factors used for the construction of the indexes with principal component.

44