rafmacalaba/datause-encoder-data huggingface dataset:
All of the indicators used for explaining the response variables mentioned above are valid candidates as proxies for hazard, exposure and vulnerability and most of them will be used in the analysis in the next section.

# **3 ASSESSING ECONOMIC DISASTER CONSEQUENCES AND RISK**

In order to identify the macroeconomic effects of disasters, we suggest comparing a counterfactual situation ex-post to the observed state of the system ex-post. This involves assessing the potential trajectory (projected unaffected economy without disaster) versus the observed state of the economy. This contrasts with observing economic performance post-event and actual performance pre-event, as usually done in similar analysis. Our analysis requires projecting economic development into a future without an event. The approach is illustrated via the case of Honduras, which was heavily hit by Hurricane Mitch at the end of 1998. In figure 3 absolute GDP with the event and projected GDP without an event were estimated. The chart exhibits GDP growth to become negative in the year after, then rebound later; yet, overall the net effect would seem to be a loss.

<!-- Start of picture text --> 8,000 GDP in Honduras<br>7,500<br>7,000<br>6,500<br>6,000<br>Projected w/o event-ECLAC<br>Projected w/o event-IIASA<br>5,500 Observed<br>5,000<br>Million constant 2000 USD<br>1996 1997 1998 1999 2000 2001 2002 2003 2004<br><!-- End of picture text --> Fig. 3: Observed GDP in Honduras with events vs. projected growth without events. Source: Zapata, 2008; World Bank, 2007; own calculations Note: Zapata (2008) uses a model based projection, IIASA projects growth statistically based on pre-disaster observed GDP.

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A second concern with aggregate indicators is that their effectiveness at reducing measurement error depends crucially on the extent to which their underlying sources provide independent information on governance. We have already discussed in Section 2 the criticism that some types of expert assessments might make correlated errors in their governance rankings, although empirical evidence suggests that these error correlations are likely not very large in practice. However, it is important to keep in mind that aggregate indicators can only mitigate the component of measurement error that is truly independent across the different underlying indicators. This point is particularly relevant when contrasting "multiple-source" and "single-source" aggregate indicators. The Worldwide Governance Indicators are an example of the former, combining information from a large number of distinct data sources. In contrast, many of the data sources reported in Table 1 report aggregates of their own subcomponents. For example, there is an aggregate CPIA rating in conjunction with the 16 underlying components, and there are six aggregate Global Integrity indicators combining information from over 200 underlying individual indicators. The distinction here is that in the latter case, all of the underlying individual indicators for a given country are scored by the same respondent. As a result, any respondent-specific biases are likely to be reflected in all of the individual indicators, and so the gain in precision from relying on the aggregate indicators from these sources will not be as large as when aggregate indicators are based on multiple underlying sources.

In summary, we have argued that aggregate governance indicators can play a useful role in synthesizing and summarizing the large variety of existing individual governance indicators. In this sense, the use of aggregate indicators is one way to exploit the complementarities between the different types of indicators that we have discussed in the previous sections (rules vs. outcomes, surveys vs. experts). A further benefit of aggregation is the increase in precision with which these aggregate indicators measure broad, although unobservable, concepts of governance. At the same time however we recognize that for some purposes more specific indicators are useful, and thus it is important to be able to easily disaggregate aggregate indicators into their constituent components, as for example is the case with the WGI.

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Figure 1. Affiliation and Contribution to the System of Individual Retirement Accounts in Chile, 1981 **-** 1999 <!-- Start of picture text --> 7000000- 80<br>6000000-- 70<br>60<br>5000000 ,<> l 6<br>11 ~~~~~~~~~~502<br>4000000I<br>-I0 2000000- j3000000--3 20 ae<br>1;<br>1000000- a.- ~ ~ ~ ~ ~ ~ ~ Ya 10<br>0 0<br>Year<br>E = Affiliates Contributors Contfibutors/Affiliates<br><!-- End of picture text --> Source: SAFP, 1999 39
<!-- Start of picture text --> Counting vector CCDF by scenario Difference between counting vector CDF curves<br>First-order stochastic dominance (α=0)<br>0 .2 .4 .6 .8 1 0 .2 .4 .6 .8 1<br>Counting vector (relative scale) Counting vector (relative scale)<br>Baseline Alternative Confidence interval (95 %) Estimated difference<br>B1.3. SOSD: Social Sustainability Index B1.4. Rank correlations<br>Social gaps Rank correlation Cutoff values<br>Social Sustainability Index by cutoff and scenario<br>Second-order stochastic dominance Metrics coefficient k = 33% k = 50% k = 75%<br>Spearman 0.98*** 0.95*** 0.93***<br>H<br>Kendall tau-b 0.94*** 0.89*** 0.83***<br>Spearman 0.9*** 0.52 0.98***<br>A<br>Kendall tau-b 0.78*** 0.39 0.94***<br>Spearman 0.93*** 0.97*** 0.93***<br>M0<br>Kendall tau-b 0.83*** 0.89*** 0.83***<br>0 20 40 60 80 100<br> Cutoff k (relative scale) ***p<0.01, **p<0.05, *p<0.1<br>Baseline Alternative<br>1 .2<br>.8<br>.1<br>.6<br>CCDF<br>.4 Difference<br>0<br>.2<br>0<br>-.1<br>.4<br>.3<br>.2<br>.1<br>Social Sustainability Index<br>0<br><!-- End of picture text --> _B2. Change in the weights assigned to each dimension. Baseline scenario: Evenly distributed weights across all indicators (25% each). Alternative scenario: Skewed weighting scheme in favor of the Resilience dimension (40% for Resilience, and 20% for the rest of dimensions)._

# Peru

<!-- Start of picture text --> B2.1. FOSD: Counting Vector B2.2. Dominance Analysis<br>Counting vector CCDF by scenario Difference between counting vector CDF curves<br>First-order stochastic dominance (α=0)<br>0 .2 .4 .6 .8 1 0 .2 .4 .6 .8 1<br>Counting vector (relative scale) Counting vector (relative scale)<br>Baseline Alternative Confidence interval (95 %) Estimated difference<br>.4<br>1<br>.8 .3<br>.6 .2<br>CCDF<br>.4 Difference .1<br>.2 0<br>0<br>-.1<br><!-- End of picture text --> B2.3. SOSD: Social Sustainability Index B2.4. Rank correlations 42
according to that graph are in the low income ranking (with the exception of Latvia and Lithuania). In 8 out of 16 ECA countries reported in Figure 3.3, GDP per capita is now below the average of that in East Asia.

**Figure 3.3. GDP per capita ppp in ECA countries compared to other regions 2001**

<!-- Start of picture text --> 18000<br>16000<br>14000<br>12000<br>10000<br>8000<br>6000<br>4000<br>2000<br>0<br>Balkans Caucasus Central Asia EU Accession Slavic<br>Slovenia<br>Czek R.<br>Hungary Slovak R.<br>Estonia<br>Croatia Poland Upper mid<br>Lithuania<br>Latvia Belarus<br>Bulgaria Kazakhs Russia ECA LAC<br>Bosnia H Macedonia Romania MNA Middle Inc<br>Albania Turkmen Ukraine East Asia<br>Azerbaijan<br>Moldova Armenia Georgia Kyrgyz R Uzbek Low inc<br>Tajikistan<br><!-- End of picture text --> Source: WDI Besides important differences in national income across countries, the region is also characterized by large disparities in national poverty levels. Each country in the region has its own country-specific poverty line, and thus the differences in so-called “national” poverty rates across countries are not strictly comparable. However, because these national rates reflect what is widely accepted as the incidence of poverty in each country (see Section 4), it is interesting to see how these vary across countries in the region with different level of national income.

Figure 3.4 presents national poverty rates and GDP per capita for several countries in the ECA region and other countries with similar level of income. What is notable is that many ECA countries (Tajikistan, Kyrgyz Republic, Armenia, Azerbaijan, Romania and Moldova) reported in the figure are above the trend line with considerable margins in terms of poverty rates. This indicates that these ECA countries have higher poverty levels than would be expected at their levels of income. However, not all countries in the ECA region face the same incidence of poverty. While national poverty tends to be the highest in Tajikistan, the Kyrgyz Republic and Moldova, it is much less pronounced in Bulgaria, Albania, Belarus, Bosnia, and Kazakhstan.

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_Notes: Graph depicts average of firm‐specific variables (Return on Assets and Leverage Ratio) for MNEs with an ownership link to a specific country (dark blue line) and the average of these variables for matched MNEs without this link_ .

Data Source: ORBIS (2016) Noteworthy observations from Figures 11 and 12 include considerably lower reported profitability ratios of MNEs with Cypriot affiliates than in the control group. In 2009 and 2010, the difference with comparable firms is particularly remarkable and statistically significant. The development of the leverage ratio is presented in the second row and shows a moderate convergence between treatment and control group, as depicted in the third row.

Another interesting observation is that MNEs with an affiliate in Russia show no statistically significant differences in the return on assets in any given year. On average, however, the profitability figures seem to be slightly higher than the returns of comparable firms, particularly before 2010. The second row indicates that MNEs with a Russian affiliate are moderately leveraged. The first column in Figure 11 contrasts MNEs with affiliates in the British Virgin Islands with a matched control group. While profitability ratios seem to be particularly low, there are no significant deviations in any year.

To further analyze whether group structures impact firm‐specific variables, we estimate a series of regression specifications (see Tables 6a and b) where we include locational dummies in addition to controlling for industry composition and size. Aggregating over years supports the trends apparent in Figures 11 and 12: groups with affiliates in Cyprus exhibit significantly lower profitability ratios. On average, we find the return on assets to be 0.82 lower than the one of comparable firms without Cypriot affiliates. The difference is significant at the 1 percent level. The regression results also indicate that MNEs with affiliates in the UK (‐0.44) and the British Virgin Islands (‐1.42) exhibit significantly lower profitability ratios.

In contrast, we find MNEs with links to Poland, Germany, and the Netherlands to be more profitable than comparable firms of similar size, with significantly positive deviations of 0.83, 0.69, and 0.86, respectively. These findings seem to be partly driven by leverage ratios. Groups with affiliates in Cyprus and Luxembourg are also highly leveraged, while groups with direct relationships to Russia and Poland exhibit a lower ratio of liabilities to assets.

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# **3 Key Characteristics and Productivity Drivers of Household Enterprises**

## **3.1 Characteristics of Household Enterprise Operators**

21. **Demographic profile** . Owing to high fertility and therefore high rates of population growth, the majority of HE operators are young, though they tend to be slightly older relative to the country‟s labor force in general (Tables A1.7 and A1.8). Over half falls in the age group 1534 years. Between 2001 and 2006, however, the share of HE operators in the age group 15-24 declined by three percentage points, while those in the 25-34 age group rose by about 3 percentage points. As will be noted later, this youngest group is also associated with low levels of education, most likely because they left school early. Urban areas have a high density of labor force in the age range 25-34 because of migration of this age group from rural areas. In Tanzania, women and men are equally likely to start an HE.

22. **Education and training profile.** The level of education of HE operators is similar to that of the labor force in general (Table A1.9). Over 60 percent of them completed primary education only. Consistent with an overall low level of education in rural areas, a higher share of HE operators in the rural areas had no education (24 percent), or completed primary education only (17 percent), compared to Dar es Salaam with 10 percent and 11 percent, respectively (Table A1.7).

23. ILFS data show that more than 80 percent of HEs have never had any training of any kind, with a mere 4 percent having attended on-the-job (onsite) training. Vocational and apprenticeship training is the main form of training for about 12 percent of HEs. The proportion of HEs without training increased, but only in the rural areas, even as the share of those who received on-the-job training rose between 2000 and 2006.

24. More educated HE operators tend to acquire training as well, indicating that training is not a substitute for lack of education. .Only 5 percent of HE operators with no education report having had training, whereas the share of those with training increases up to 68 percent for the 5 percent of HE operators with advanced secondary or university-level education (Figure 3.2). Recent research suggests that even in the informal apprenticeship system where the formal education requirements may appear less stringent, formal education is an important determinant of HE operators‟ acceptance into training programs.<sup>6</sup> This raises concerns about their opportunities for further skill acquisition, let alone their prospects for formal sector employment. Fox and Gaal (2008) argue that at least some post-primary education is generally required to obtain a job in the formal sector or to upgrade their skills.

> 6 See Kahyarara and Teal (2008)

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**Table 15: Determinants of energy consumption (2002-2004, Non CRS, Pooled effects)**

|**Dependent**<br>**variable=ln(energy/output)**|**Four indust**|**ries**|**Pulp and**<br>**indu**|**Paper**<br>**stry**|**Cement ind**|**ustry**|**Iron and**<br>**indus**|**Steel**<br>**try**|**Aluminum i**|**ndustry**|
|---|---|---|---|---|---|---|---|---|---|---|
||Coef.|P-value|Coef.|P-value|Coef.|P-<br>Value|Coef.|P-<br>Value|Coef.|P-<br>Value|
|**Constant**|1.4423|0|1.8669|0.007|1.7057|0|-1.4922|0.076|0.2480|0.812|
|**Ln(price of energy/price of**<br>**output)**|-0.7373|0|-0.5032|0.006|-0.4571|0|-0.7060|0.018|0.6096|0.466|
|**Ln (technology development)**|-0.0376|0|0.0026|0.852|-0.0125|0.001|-0.0541|0.046|-0.049|0.459|
|**Ln(Value of industry output at**<br>**constant price)**|0.803|0|0.7290|0|0.8264|0|0.9783|0|0.8732|0|
|**Ln(Price of energy/price of**<br>**output)*Ln(technology**<br>**development)**|-0.039|0|-0.0040|0.818|-0.0189|0|-0.0461|0.087|-0.155|0.102|
|**Foreign capital intensity**|0.111|0.73|-0.5649|0.306|-0.1244|0.727|4.4088|0.212|-17.97|0.34|
|**Foreign capital**<br>**intensity*Ln(technology**<br>**development)**|-0.027|0.115|0.0134|0.794|-0.0320|0.032|-0.3858|0.219|1.4775|0.333|
|**Collectives**|-0.5292|0|-0.0810|0.757|-0.3927|0|-0.9783|0|-0.067|0.84|
|**Foreign**|-0.373|0.087|0.4846|0.159|-0.2611|0.365|-0.9321|0.058|-0.035|0.97|
|**Hong Kong SAR, China; Macao;**<br>**Taiwan, China**|-0.3337|0.002|0.1265|0.527|-0.1607|0.044|-0.0738|0.825|-0.758|0.007|
|**Shareholding**|-0.1973|0.003|0.2011|0.173|-0.2224|0|-0.2653|0.132|-0.515|0.003|
|**Private**|-0.232|0.067|0.1747|0.568|-0.1572|0.027|-1.1345|0.053|-0.964|0.009|
|**Other**|(omitted)||(omitted)||(omitted)||(omitted)||(omitted)||
|**North**|-0.1913|0.115|-0.5328|0.065|-0.2319|0.005|0.9526|0.053|(omitted)||
|**Northeast**|(omitted)||(omitted)||(omitted)||0.8432|0.099|-0.344|0.42|
|**East**|0.0375|0.733|-0.1876|0.462|-0.1502|0.039|0.9879|0.034|-0.156|0.542|
|**South**|0.1283|0.254|0.1239|0.618|-0.0747|0.313|0.9083|0.064|-0.087|0.758|
|**Southwest**|-0.9787|0|(omitted)|--|-0.1245|0.548|(omitted)|--|-0.596|0.128|
|**Year 2003**|-0.0877|0.187|-0.0361|0.771|-0.0263|0.563|-0.2583|0.127|-0.098|0.501|
|**Year 2004**|-0.0663|0.326|-0.0504|0.687|0.0477|0.309|-0.353|0.04|-0.129|0.408|
|**R**<sup>**2**</sup>**(obs.)**|0.7539 (758|)|0.7429 (14|4)|0.8512 (335|)|0.7801 (20|8)|0.8973 (71)||

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# **Table 2B: Growth and Uphill Flows of Sophisticated Exports** **<u>(Panel Regressions; Sophisticated exports to richer countries scaled by GDP)</u>**

||(1)|(2)|(3)|(4)|(5)|
|---|---|---|---|---|---|
|Variable|Random<br>effects|Fixed<br>effects|Fixed<br>effects|Fixed<br>effects|Fixed<br>effects|
|Per capita GDP (log)|-0.007***<br>(0.003)|-0.063***<br>(0.012)|-0.063***<br>(0.012)|-0.060***<br>(0.012)|-0.042***<br>(0.008)|
|Uphill export flows of sophisticated (median)<br>products (as share of GDP)|0.229***<br>(0.055)|0.231***<br>(0.085)|0.226**<br>(0.090)|||
|Downhill export flows of sophisticated (median)<br>products (as share of GDP)|-0.024<br>(0.031)|0.056<br>(0.049)|0.063<br>(0.052)|||
|Years of primary schooling|0.019***<br>(0.007)|0.021<br>(0.019)|0.021<br>(0.019)|0.022<br>(0.020)|0.052***<br>(0.018)|
|Exports of non-sophisticated (median) products<br>(as share of GDP)|||0.012<br>(0.035)|||
|Uphill export flows of sophisticated (75th<br>percentile) products (as share of GDP)||||0.264<br>(0.197)|0.489**<br>(0.205)|
|Downhill export flows of sophisticated (75th<br>percentile) products (as share of GDP)||||0.059<br>(0.127)|0.081<br>(0.151)|
|Exports of non-sophisticated (75th percentile)<br>products (as share of GDP)||||0.037<br>(0.034)|0.034<br>(0.036)|
|Observations|258|258|258|258|258|
|Number of rcode|64|64|64|64|64|
|R-squared|0.13|||||
|Adjusted R-squared||0.288|0.285|0.271|0.171|
|F-test||5.92|5.19|4.82|5.59|

Robust standard errors in parentheses *** p<0.01, ** p<0.05, * p<0.1 All columns, except column 1, include time effects 30
**Table 3. Comparing the impact over time**

|VARIABLES|(1)<br>Exchange<br>Rate<br>Depreciation|(2)<br>NEER<br>(%<br>change)|(3)<br>REER<br>(%<br>change)|(4)<br>%<br>Change<br>in<br>Reserves|(5)<br>% Change in<br>Equity prices<br>(real)|(6)<br>GDP<br>growth<br>(quarterly<br>yoy)|(7)<br>Investment<br>Growth<br>(quarterly<br>yoy)|(8)<br>Current<br>Account<br>Balance % GDP|
|---|---|---|---|---|---|---|---|---|
|Sudden Stop|11.08**|9.15**|8.77***|-12.24**|-3.20|-3.78***|-11.64***|1.67|
||[2.58]|[2.29]|[3.53]|[2.65]|[0.96]|[3.39]|[2.88]|[1.52]|
|Dummy 2003-2014|-4.51***|-2.68*|-0.11|-0.39|2.81***|0.77*|0.48|-0.20|
||[2.92]|[1.80]|[0.41]|[0.90]|[4.52]|[1.78]|[0.29]|[0.24]|
|Sudden Stop *|||||||||
|Dummy|||||||||
|for 2003-2014|-3.08|-4.93|-5.29**|4.26|-7.89*|-1.47|0.92|-0.76|
||[0.69]|[1.21]|[2.06]|[0.84]|[2.04]|[0.99]|[0.15]|[0.52]|
|Constant|4.46***|-2.44***|0.30|2.86***|-1.64***|3.78***|7.78***|-1.51**|
||[4.71]|[3.23]|[1.58]|[8.84]|[3.78]|[12.73]|[7.09]|[2.74]|
|Observations|2,569|1,926|2,159|2,573|2,284|2,160|1,959|2,000|
|R-squared|0.054|0.05|0.076|0.023|0.026|0.081|0.031|0.005|
|Number of|||||||||
|countries|34|26|28|34|31|32|29|30|
|Adj. R-squared|0.053|0.048|0.075|0.022|0.025|0.079|0.030|0.003|

Data are quarterly over the period 1991-2014. Dependent variables are as indicated in the first row. All variables are in percentage. GDP growth and investment growth are year-over-year. Regressions include country fixed effects. Robust t statistics are in parentheses. *, **, or *** indicate the coefficients are significant at 10, 5 or 1 percent level of significance. Regressions with year fixed effects instead of a different intercept for post 2003 period yield similar coefficients.

20
estimating consumption poverty and inequality in that we also combine survey data with unit record census data in order to obtain estimates at a lower level of aggregation than the survey permits. Their small area estimation approach was first applied to Ecuador (Hentschel et al., 2000) and has subsequently been applied to poverty and inequality measures in many countries, including Cambodia (Alderman et al., 2002; Demombynes et al., 2002; Fujii, 2004)). We first provide a general overview of the methodology, and then describe it more formally.

The basic idea is straightforward. We first construct a prediction model of anthropometric indicators using only the variables that are common between the census and the survey, along with geographic indicators available for the entire country at the village or commune level. The geographic indicators include the remotely-sensed data as well as village-level statistics derived from the census data. Common geographic codes in all data sets allow these to be linked to both the survey and the census data.

The parameters of the model are estimated with the survey dataset. An important feature of our study and of the ELL approach is the explicit treatment of the error terms. We estimate regression coefficients and the associated variance-covariance matrix, and also scrutinize the distribution of the disturbance terms in order to carry out simulation. In each round of simulation, we randomly draw regression coefficients and disturbance terms in accordance with their estimated distribution and we impute the anthropometric indicators to each census record. By geographically aggregating the imputed anthropometric indicators, we can estimate the prevalence of malnutrition.

There are four major differences between this study and earlier work by ELL. The first and most obvious difference concerns the type of the survey dataset used for estimation. ELL have focused on consumption or income taken from a socio-economic survey, while we have anthropometric measures taken from a Demographic and Health Survey (DHS). A second difference stems from different units of analysis. Consumption data are usually produced at the household level, whereas anthropometric measures are at the individual level. In the 7