rafmacalaba/datause-encoder-data huggingface dataset:
the trade balance of advanced economies improves by more than when the emerging country spending is purely on goods and services.

**Figure 3: Growth and rebalancing implications of infrastructure versus current spending**

**GDP: % deviation                                                               Trade balance: % GDP deviation**

<!-- Start of picture text --> 8 1<br>7<br>0.8<br>6<br>5 0.6<br>4<br>3 0.4<br>2<br>0.2<br>1<br>0 0<br>-1 2010 2012 2014 2016 2018 2020 2022 2010 2012 2014 2016 2018 2020 2022<br>Developing World United States High Income<br>Infrastructure Infrastructure<br>Current Current<br><!-- End of picture text --> Source: McKibbin, Stoeckel, and Lu (2011). Simulations with G-cubed model. All results are expressed as percent deviations from baseline.

42. A global infrastructure initiative, if properly designed and implemented, could raise exports from and reduce unemployment in high-income countries, while reducing poverty and enhancing growth in developing countries. Infrastructure investment would raise the demand for capital goods from capital good-exporting countries, most of which are advanced economies, as well as raise their exports, employment, GDP, and, ultimately, fiscal revenues. It would also contribute to a diversification of the export base of some capital goods-exporting emerging economies, such as China, reducing their dependencies on export demand from a few highincome countries. A global infrastructure initiative could generate a virtuous cycle of global growth. As the income of developing countries is raised, their import demand for products produced around the globe would increase. Boosting exports in advanced economies would not only reduce unemployment and lift their growth, it would also reduce external borrowing needs, potentially unleashing more surplus global savings in support of investment and growth in developing countries. This in turn would lead to additional investment opportunities and potentially open up new markets. Ultimately, this could create a virtuous, self-reinforcing cycle where surplus global savings flows to support investment and growth in developing countries, which in turn would generate more import demand, thereby reinforcing global growth (Qureshi 2011). Such an initiative could support the global crisis recovery and help the world economy become more inclusive and stable.

19
subsection. For each question, we create a binary variable to identify the correct response and perform PCA analysis based on polychoric correlations, following the method developed to adapt PCA to ordinal data by Kolenikov and Angeles (2004). We estimate the financial literacy index as the first principal component of the four financial literacy questions. The procedure is described in greater detail in Appendix A for the year 2008 (the analysis for 2009 is available upon request). The corresponding distribution of eigenvalues is presented in Appendix A, Panel B (the first component accounts for 53% of the variation) and the factor loadings/scoring coefficients for the index are detailed in Panel C.<sup>11</sup> The financial literacy index distribution is shown in Panel E.

# 4.4.2 _<u>Who Is Financially Literate in Russia?</u>_

Table 1, columns 2 and 3, show summary statistics for individuals with high and low levels of financial literacy; asterisks indicate significant mean differences. We identify ―high‖ financial literacy as individuals with a financial literacy index greater than the sample median. Univariate tests find that financially literate individuals are more likely to be male, married or cohabiting, younger, and residents of urban Russian regions. They are more likely to have vocational/technical, or some level of higher education, and be employed in skilled or non-manual occupations. Importantly, individuals in the lowest income quartile are more likely to score low in terms of their financial literacy, while those in the highest income quartile are more likely to be highly financially literate. This is consistent with many other surveys on financial literacy in other countries (see Lusardi

> 11 Although we retain the first factor for the purpose of parsimony, the optimal number of factors, identified by Humphrey-Ilgen parallel analysis (Lance, Butts, and Michels, 2006), is in fact two. We compare the number of factors derived from our survey data against factors for random numbers representing the same number of cases and variables and obtain the optimal number of factors at the intersection of plots of factors against cumulative eigenvalues for the two sets of data (Panel D).

17
# **Prioritizing Heat Mitigation Actions in Indian Cities: A Cost-Benefit Analysis under Climate Change Scenarios**

Nicholas Jones<sup>1</sup> , Asmita Tiwari<sup>2</sup> , Natsuko Kikutani<sup>2,</sup> Sacha Takacs<sup>3</sup> , Niels Souverijns<sup>3</sup> JEL Codes: Q54, Q51, R58, I18, O18 Keywords: Benefit-cost analysis, public health, labor productivity, climate change

> 1 Global Facility for Disaster Reduction and Recovery, World Bank

> 2 Global Practice for Urban, Resilience and Disaster Risk Management, World Bank

> 3 Vlaamse Instelling voor Technologisch Onderzoek (VITO)
in the case of use of a sophisticated production technology that can be visually identified in the shop floor.

# **A.6 Ex-post Checks and Validation Exercises**

In addition to using best practices in survey design and implementation, it is important to perform validation checks once the data is collected. This allows us to measure the effectiveness of all these efforts to minimize bias and measurement error. In what follows, we describe some of the validation tests performed.

**Minimizing potential non-response bias** Our survey implementation was designed to minimize non-response through the use of well-prepared agencies and institutions to administer the survey and the presentation of adequate supporting letters to encourage participation. Table A.17 shows response rates by country, firm size group and sector. Response rates vary between 15% in Croatia and 86% in Georgia.

Table A.17: Response rates (by country)

|Country|Response rate|
|---|---|
|Bangladesh|30%|
|Brazil|39%|
|Burkina Faso|45%|
|Cambodia|16%|
|Chile|40%|
|Croatia|15%|
|Ethiopia|42%|
|Georgia|86%|
|Ghana|49%|
|India|49%|
|Kenya|77%|
|Korea*|24%|
|Poland|47%|
|Senegal|57%|
|Vietnam|80%|
|**Average across countries**|**46%**|

These are unweighted response rates calculated as the ratio between firms that responded the survey and the total number of firms in the sample which we attempted to conduct the interview. The high response rate for Vietnam is associated with the fact that the survey was implemented by the national statistical office. In most cases, these response rates are high relative to typical response rates in firm-level surveys, which for the U.S. are around 5 to 10 percent, and are consistent with response rates observed for WMS and MOPS (Bloom 105
so 22. In Cote d'Ivoire and Morocco, the amount of food marketed by the<sup>poor is quite small, so</sup> this effect is not likely to be very significant.

23. Because of the fixed exchange rate, increased competitiveness<sup>was achieved by the involuntary</sup> appreciation of the dollar and by the voluntary depreciation<sup>of the French franc against other EMS</sup> currencies in 1981, 1982 and 1983, and, later in the adjustment,<sup>by the combination of a tariff</sup> surcharge and export subsidies. In Cote d'Ivoire, little increase<sup>in competitiveness was achieved</sup> indirectly.

24. For an assessment of recent tax reforms, including those for Indonesia<sup>and Morocco, see</sup> Thirsk (1990).

_25._ Definitions of country groupings are given in table 1. A fairly similar pattern<sup>of results</sup> obtains with groupings defined by income levels. Note, however,<sup>that all the data in table 2 are from</sup> the World Bank. While internally consistent to the extent<sup>possible, the period averages in table 2</sup> might deviate slightly from those that would be obtained from the data in the country<sup>studies.</sup> 26. See the article by Meller in this issue (table 4) for a decomposition of<sup>the primary and</sup> secondary costs of adjustment in terms of tradable and nontradable<sup>sectors. It should be noted that</sup> Chile had widespread wage indexation in the formal labor market<sup>which contributed to inflexibility.</sup> 27. In fact, Morocco tried to stabilize attempts in 1978 ard 1980, but govermment<sup>attempts to cut</sup> food subsidies, led to riots and to the abandonment of the measures.<sup>See the article by Morrisson in</sup> this issue for further discussion.

28. Both countries are among a group of 18 highly indebted countries.

29. In terms of figure 2, these supply-augmenting structural reforms moved<sup>both countries closer</sup> to their respective production possibility frontiers.

30. Eventually Indonesia received adjustmnent loans from the World Bank (starting in 1987, but by then, most adjustment measures had already been adopted.

31. With the election of Cordero in 1984, political power shifted back<sup>toward the traditional elite,</sup> agroexporters and bankers 32. Industrial employment fell at an average annual rate of 2 percent during<sup>1981-85. See the</sup> article by de Janvry, Sadoulet, and Fargeix in this issue (table 2).<sup>They note that fiscal also austerity</sup> reduced employment opportunities for skilled and unskilled labor.

33. It is particularly encouraging that the base run of the Malaysia model<sup>tracked very closely the</sup> observed changes in the distribution of income during adjustment. See the<sup>article by Demery and</sup> Demery in this issue (section 6).

34. Morrisson simulates earlier adjustment with a package that includes a freeze on<sup>the minim'zm</sup> wage. While such a policy is likely to be more acceptable when applied early<sup>on, it would still</sup> probably meet with resistance.
in the whole Accra (0.48) (Engstrom et al., 2017). In addition, some EAs are excluded from the sampling frame for political sensitivity reasons or security concerns for enumerators after consulting local experts.<sup>3</sup> More details are provided in Appendix 1.

_Table 1: Profiles of EAs in the selected neighborhoods in flood risk and slum index_

|Neighborhood|mean elevation<br>(meters)|Share of EAs with<br>low elevation(%)|Share of EAs with<br>high elevation(%)|
|---|---|---|---|
|Gbegbeyise|5.55|100%|0%|
|Korle Lagoon Area|7.35|100%|0%|
|Jamestown|11.54|100%|0%|
|Korle Dudor|12.01|100%|0%|
|PigFarm|21.09|42%|8%|
|Banana Inn|24.37|0%|0%|
|Nima|29.63|0%|31%|
|Accra New Town|32.38|0%|34%|
|Mamobi|33.34|0%|37%|
|Abeka|33.57|0%|43%|

At the time of the sampling, there was no reliable flood map for the 2015 event that was representative on a city level for Accra. (Since then, such a map has been developed, see Klopstra et al., forthcoming.) As a result, we used elevation as a proxy for flood risk in our sampling strategy. Using this proxy was supported by measurement of flood height data from the 2015 flood event that were collected by a group of researchers from TU Delft University after the 2015 flood event. Using these estimates, we classify EAs into high risk (elevation below 17.5 m), medium risk (between 17.5-34 m) and low risk (above 35 m). We then removed the EAs in the medium risk category, to obtain more contrasted results. We end up with 145 EAs across the 10 selected neighborhoods. The 145 EAs were the sample frame of this study, and the results are thus applicable to the population living in those EAs.

Since there are no data on poverty at the EA level, we used neighborhood level poverty estimates to classify the EA as “high poverty” or “low poverty.” The neighborhood level poverty rates were estimated using small-area poverty estimation methodology carried out by Engstrom et al. (2017) using GLSS6 and geospatial data and census data. EAs with a neighborhood poverty rate higher than the median (5.8%) are categorized as “high poverty areas” and those with a poverty rate lower than the median are categorized as “low poverty areas.” We selected 24 EAs from low elevation (12 “poor” and 12 “non-poor”) and 24 EAs from high elevation (12 “poor” and 12 “non-poor”) using Probability Proportional to Size (PPS). Subsequently, we randomly selected 20 households from each EA<sup>4</sup> to arrive at a sample of 1,006 households living in areas officially defined as slums with different levels of flood risk exposure. The map in

> 3 Sabon Zongo and Gomorrah were excluded from the sampling frame for this reason.

> 4 The number of households was chosen to ensure that differences in key statistics across the four groups of EAs could be significant at a certain level of confidence (statistical power calculation approach).

8
(2006). Cruces and Trebesch (2013) estimate a 37% average haircut for the sample period 1978– 2010 and a 50% average haircut during 1998–2010.

# 3.5. Strong association between financial stress and banks’ total exposure to the public sector

Regression estimates suggest that financial sector and banks’ total exposure to the public sector are strongly associated, even after controlling for global financial conditions (i.e., the VIX index). Spreads on sovereign dollar‐denominated debt appear to price this in (Table 2). To explore this link, we run panel regressions with country‐ and year‐fixed effects (with standard errors clustered at the country level) to explain the median probability of default (PD) of financial institutions within the next 60 months.

- In a sample of 52 EMDEs for months in the period 2000 ‐ 2017, we find a strong association with banks’ total exposure to the public sector to GDP (this includes central, state, and local government and public non‐financial entities). Regressions 1 and 2 suggest that a one percent increase in public exposure raises the PD by 0.16%, even after controlling for the VIX, a widely used proxy for global financial stress and risk appetite. This effect is economically relevant since the sample means for banks’ total claims and the PD are 11.7% and 2.2%, respectively, and both exhibit substantial cross‐country variation.

- In a smaller sample of 36 countries, Regressions 3 and 4 show that the EMBI spread is associated with the PD as well, although the effect diminishes once the VIX is controlled for. This may also in part be explained by the fact that the PDs are estimated from equity prices which themselves depend on global financial conditions.

- Regressions 5, 6, and 7 show that banks’ total exposure is no longer significant in the presence of the EMBI spread and an interaction. This suggests that international investors already price in the effects of bank exposures through the EMBI spread.

25
units. The only difference in the design is that the second step has a nonrandom assignment. There are now four distinct groups, depending on unit type and eligibility type: eligible subjects (E=1) in treatment (T=1) and control (T=0) units and ineligible subjects (E=0) in treatment (T=1) and control (T=0) units.

Treating the eligible subjects may affect ineligible subjects’ outcomes. Therefore, as long as we observe outcomes for the four groups we can measure the treatment effect on both eligible and ineligible subjects: These comparisons identify the two average treatment effects as long as: (1) the unit randomization works, that is, there are no ex-ante systematic differences in the characteristics of eligible subjects in treatment and control units and of ineligible subjects in treatment and control units; and (2) any spillover effect of the treatment occurs only within the treated unit.

For example, <mark>the Kenya deworming project excluded older girls (ages 13 and older) from deworming due to the risk that the drugs could cause birth defects.</mark> To measure spillover effects, the first step is, as before, to randomly group schools into a treatment and control group. The second step is to offer the drug to all boys and younger girls in treatment schools. The four groups are boys and younger girls (E=1) and older girls (E=0) in treatment (T=1) and control (T=0) schools. There may be spillover effects of the deworming drug among the older girls attending the treatment school. Therefore, one should collect data on all pupils in the two groups of schools. In this way, one can measure the effect of offering the drug to boys and younger girls on the infection rates of both eligible and ineligible students.

In some cases, determining eligibility is more complex and involves the collection of multiple variables. For example, for conditional cash transfer (CCT) programs, eligibility is determined by poverty status. In this case the evaluator needs to collect enough data to compute a continuous poverty indicator for both eligible and ineligible subjects in treatment and control units. This process of ex-ante data collection for eligible and ineligible subjects, as well as ex-post collection of information on the outcomes of interest, makes the estimation of spillover effects more costly and time consuming.

# **3.4 Understanding the mechanisms**

A well-conceived evaluation design should be based on some hypothesis about whether
21 mid-80s. Germany's share has been 35 to 45 % over the past two decades. Japanese data on private operating costs are not available. Investment expenditure is likely to dominate, however, as most public environmental expenditure was in the form of investments (91 %). In the Nordic countries, estimates of the share of investment in total expenditure are poor. The share of investment in total expenditure in manufacturing in Finland has been around 40% in the 1980s. For Sweden and Norway, few estimates are available. In the mid-1980s the share in Sweden would have been above 50% and in Norway around 30%.

**Table 12; Sh 're of investment in public and private environmental expenditure, seiected industrial countries, mid-1980's**

||Public|Private|
|---|---|---|
|||**(%)**|
|Austria|**n.a**|56|
|**Fuiland**|44|48|
|Norway|32|33|
|Sweden|32|53|
|**Gerrmny**|47|42|
|**Japan**|96|**na.**|
|US|38|**38**|

Source: OECD (1990).

The higher share of investment in total environmental expenditure in European countries in recent years as compared to the United States can explain their increased competitiveness in many environmentally sensitive goods and machinery. In Austlia, wliich showed positive correlation between trade performance and environmental expenditures, the share of investments in total has also increased. Better trade performance in environmentally sensitive goods in Europe coupled with success in reducing major pollutants (see below) suggest ,..at the European countries may have been more successful in internalizing environmental costs and promoting technological change in environmentally sensitive products. But the high public investment in Japan did not improve its comparative advantage in environmentally sensitive industries. They can, however, explain Japan's success in developing appropriate technologies and exporting machinery to the sensitive industries (see below).
5 explained variation, probability of rejecting a linear relationship between dependent and independent variables, and significance of the coefficients on saving).

The other decision required for estimation concerns the discount rate. The underlying theory (see Ch. 3 and Ferreira _et al._ (2003)) suggests that the rate should be the marginal product of capital less depreciation rates for produced capital, less population growth rates, which argues for a low value. We use a uniform rate of 5%, and tests of alternatives suggest that the estimates are fairly insensitive to small changes in the discount rate.

Allowing for the sparse early-1970s savings data<sup>4</sup> , therefore, expression (4) was estimated using OLS for consecutive 20 year periods from 1976-1980. These results, as well as more informal methods, are reported below.

# **Empirical results**

To give a feel for the data, we first scatter the present value of changes in consumption against the four different savings measures for 1980 in Figures 1-4. The broad picture which emerges is that there is no monotonic improvement in the fit with theory as more stringent measures of saving are applied. The coefficient on saving actually drops going from gross saving to net saving, and the explained variation drops considerably. For genuine saving the coefficient on saving is very near 1 and the explained variation is the highest of the four saving measures. Finally, for Malthusian saving the coefficient on saving drops to the lowest level of the four measures, while explained variation reaches its highest value.

Figure 5 presents the same scatter for high income countries only. As seen in Ferreira and Vincent (forthcoming) and Ferreira _et al._ (2003), the model fit is particularly poor for these countries – further tests show the coefficient on saving to be insignificant, while the explained variation is very low.

Table 1 presents the results of the individual OLS estimates of the model for each of the five years and four measures of saving. This table reports the coefficient values with t-statistics, r-squared, degrees of freedom, the probability of rejecting a linear relationship (from the F statistic) and a simple two-sided t-test of whether the coefficient on saving is equal to 1 (values greater than 2.00 imply the coefficient is significantly different from 1 at the 5% confidence level). While there is some heterogeneity in the results, the following broad conclusions hold:

- The results for 1977 are the weakest of the five years, with low r-squared, higher probabilities of rejecting a linear relationship than other years and two saving coefficient estimates that are significantly different from 1 (although the coefficient for net saving is not itself significant). This suggests some systematic shock being picked up by the data for this year.

> 4 From 1970 to 1975 there are fewer than 40 countries with the necessary data, and these are primarily developed countries.