rafmacalaba/datause-encoder-data huggingface dataset:
limited variables to capture this information, the analysis suggests that "having a checking or savings account" is also positively associated with increasing the use of digital technology and having online sales. Using similar data from WBES, Amin and Viganola (2021) show that firms with better access to finance were significantly less likely to experience a decline in sales during COVID-19.<sup>16</sup> An important element is that even after controlling for proxies that aim to capture potential drivers of adoption, results still suggest a robust gap between firm size groups in the participation in digital markets. For investment in digital solutions, size seems the most robust observable characteristic for which significant differences are observed. In terms of sectors, ICT, retail and wholesale, and food services are among those with a more robust response towards a significant increase along different dimensions of digital adoption. Retail and food services are typically characterized by having a large share of SMEs and hiring a large number of workers.

Even after controlling for several factors, there is low explanatory power about the decision of starting or increasing the use of digital technologies. To investigate this issue, figure 17 presents a Shapley decomposition for differences of the estimations described in columns (3) and (6) of Table 2. This exercise decomposes these differences by factors attributable to specific sources. The results show that differences in observable characteristics of the firm and differences across firms do not explain a large portion of the observable differences in digitalization indicators. Furthermore, differences across countries explain only 7% of the differences in digitalization and about 90% remains unexplained. This result highlights the relevance of having a more in-depth analysis of how and which technologies are adopted by firms. For example Cirera, Comin, and Cruz (2022) show that technology adoption heterogeneity is not only large across similar firms but also within firms; across business functions inside the firm. Taking into account this heterogeneity is a key step to design better support mechanisms for firms’ digitalization.

> 16The authors used as a proxy for access to finance the average of firms within sector/size/region that apply for a loan and the loan amount was approved in full or did not apply for a loan because it had enough internal funds.

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estimates.<sup>2</sup> Originally intended to be used to estimate maternal mortality, these modules record the survival status of all main female respondents’ siblings, and include the current age if alive, and record the year of death, and age at death, in the event that a sibling has died. Subject to various assumptions and adjustments (described in more detail below) the responses can be extrapolated to form an estimate of national-level mortality.

# **2.1)  Estimating mortality based on sibling reports**

At one level using sibling reports to estimate adult mortality is straightforward. The nationally representative DHS identifies all women aged 15-49 as respondents.<sup>3</sup> These women list all of their siblings born to the same biological mother, the birth dates of those siblings, report on the survival status of those siblings, and on the age and date at death if relevant. For any timeframe, therefore, we can establish which siblings were alive at the beginning of the timeframe, and whether or not they died during that timeframe. The number of deaths during the timeframe, divided by the population at risk at the beginning of the timeframe, determines the timeframe-specific mortality rate. To be more concrete, consider the timeframe of 1995-1999. We establish the population at risk at the beginning of 1995 (P1995), calculate the number of deaths between 1995 and 1999 (D1995,1999), and estimate mortality as (M1995,1999) the ratio between the two. The generic form of this relationship is given in equation (1) where _t_ is the beginning of the timeframe of interest. We are able to cover the periods 19751979, 1980-1984, 1985-1989, 1990-1994, 1995-1999, and 2000-2004 using these data. Not all surveys provide information on all the periods as we require that the full period be captured by the survey for it to be included (that is, a survey conducted in 1997 will not contribute to the estimate of mortality in the 1995-1999 or 2000-2004 periods).

> 2 Not all DHS surveys include the sibling mortality module.  We restrict ourselves in this analysis to those that do. 3 In some countries this is all ever-married women.

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# **5.4 Informal Sector Training (IST) for HEs**

105. It is difficult to obtain a comprehensive view of informal sector training (IST) in Tanzania because of the fragmentation and dispersal of its coverage. Government policies recognize the importance of IST, but these policy pronouncements have not been translated into concrete programs or investments **_._** Moreover, no single agency has the responsibility for collecting information about IST programs. The only evaluation identified was an early assessment of the ITEP pilot training program conducted by VETA/GTZ. This evaluation did not systematically determine the costs and benefits of the program, and was conducted too early to ascertain labor market outcomes and impact on income. This section relies on a review done by Johanson and Wanga (2009).

106. Demand-driven traditional apprenticeship is the most common form of IST, but it is not well organized in Tanzania, compared to those offered in other countries in the region, particularly in Kenya, Zimbabwe, or West Africa. Traditional apprenticeship has weaknesses – it is generally regarded as static and does not keep up with advancement in technology, although instances were found of innovation in the content of apprenticeship, particularly in areas where technology is changing fast (e.g., in vehicle maintenance). Apprenticeship training in the informal sector, however, has been effective in imparting trade skills to thousands of mostly male youth, but less to female workers. Figure 5.1 shows the result of a 10-year old survey of apprenticeships in Dar es Salaam, which found that only about one in four apprentices were female, half of whom were trained in tailoring, catering, childcare, and hairdressing (Nell and Shapiro, 1999). In the 2006 ILFS, 8 percent of women, compared to 16 percent of men, reported having participated in apprenticeship training programs. In general, apprenticeship training was found to perpetuate traditional gender-based occupational segregation.

**Figure 5.1:  Distribution of Apprentices in Dar es Salaam, by Field (percent)**

<!-- Start of picture text --> Informal sector apprentices in Dar es Salaam by field - 1999<br>(% of total surveyed)<br>25<br>20<br>15<br>10<br>5<br>0<br>Motor Vehicle Welding Carpentry Tailoring Masonry Hairdressing Catering Childcare<br>Mechanics<br><!-- End of picture text --> _Note_ : _Dark bars are mainly male; white bars are mainly female; striped bar is both male and female. Source: Johanson and Wanga, 2009, based on Nell and Shapiro, 1999._ 46
the household model as the coverage rate falls to 59 percent, as opposed to 82 percent for the subarea model and 67 percent for the area-level model.

Figure 4 shows the boxplot for relative bias. All estimates are slightly biased downward. This is because they are estimated using 2016 data, which is also used for the benchmarking procedure, and then compared against 2015 estimates generated using the 2014 sample data. Therefore, it is not surprising that estimates based on 2016 would be systematically below the benchmark estimates, reflecting the overall trend of poverty decline. Nonetheless, the results confirm that the household-level model generates less biased results than the sub-area and area-level models in sample, which each give less biased results than the direct estimates. Meanwhile, the householdlevel model does at least as well as – if not slightly better than – the sub-area and area-level models out of sample. When using the 2016 sample data to estimate the model, the estimates produced by the household model are less biased than those produced by sub-area and area level models and represents a considerable improvement in accuracy and efficiency over the direct estimates. However, the uncertainty appears to be substantially underestimated, which is not as true for the sub-area estimates. Thus, while accuracy is best for the household-level model, the sub-area model outperforms in terms of correctly estimating uncertainty.

# _6.5 Including predictors at various levels._

The baseline specification includes AGEB-level variables, municipal-level variables, and statelevel dummies as candidate predictors. To better understand the sensitivity of the household model to including variables at different levels, we estimate five additional models, representing different combinations of including or excluding AGEB, municipal, and state dummies. Two models use only AGEB or only municipal candidate predictors without state dummies, two more only AGEB or only municipal candidate predictors with state dummies, and one uses AGEB and municipal predictors without state dummies. In all cases, we use plug-in LASSO to select a model from the set of candidate predictor variables.

Table 8 reports the results from this exercise. The in-sample results are very robust to different model specifications, as correlations with the benchmark vary only from about half a point. On the other hand, the accuracy of out-of-sample predictions are much more sensitive to the set of candidate predictors. The baseline specification, which includes state dummies, AGEB and 30
# **1. Introduction**

In the Caribbean, disasters caused by natural hazards are both frequent and costly. Many Caribbean countries lie in the path of destructive Atlantic hurricanes and are regularly threatened by tropical cyclones. The estimated human and economic toll of these tropical storms is substantial. For example, 2017 Hurricane Maria killed dozens and came at an estimated cost of 226 percent of GDP on the island of Dominica (Government of the Commonwealth of Dominica, 2017). Global warming is compounding the problem by making these extreme weather events more frequent and severe.

While the Caribbean is frequently in the news during the hurricane season, tropical storms are not the only natural hazard threatening the region. Seismic and volcanic activity is relatively high. As the Caribbean islands are located on the boundary of two tectonic plates, the region is prone to earthquakes, volcanic eruptions, and Tsunamis (see, e.g., U.S. Geological Service, 2018; University of the West Indies (UWI) Seismic Research Centre, Undated). Haiti, for instance, was struck by a catastrophic earthquake in January 2010. Estimates of the human death toll range considerably but point to at least 100,000 casualties. The aftermath of the earthquake was devastating too; many households had lost their homes and belongings, and gender-based violence and food insecurity increased significantly (Kolbe et al., 2010).

With climate change, rainfall patterns in the Caribbean are becoming increasingly erratic and flood risks are increasing. Suriname, for instance, experienced heavy rains at the beginning of 2022, usually a dry time of the year. The resulting floods, especially in the interior of the country, forced the prolonged closure of schools and medical posts. Other examples of floods, droughts, and landslides in the region abound. In total, since 1950, according to Ötker and Srinivasan (2018), hundreds of disasters have hit small states in the Caribbean, “killing 250,000 people and affecting more than 24 million through injury and loss of homes and assets”.

Preparing for – and responding to – these disasters, requires a people-centered approach and a strong understanding of households’ perceived risks and ability to cope with risks (e.g., U.N. Office for Disaster Risk Reduction, 2023; Upperman et al., 2019). However, many of the Caribbean countries can be considered ‘data scarce’ and there is little evidence on household preparedness for disaster risk from household survey data. With that in mind, this paper provides primary evidence on both households’ exposure to disasters and their ability to cope with these disasters. The paper is based on novel survey data collected in five countries in the region: 1
**28**

value in our sample is **Egypt** (24). The United States has a value of **71,** which is well above the mean value of **61.**

# B. **Determinants of Financial Intermediary Development and Growth**

This section examines the links between the legal environment and financial intermediary development and traces this link through to long-run growth. First, note that the legal origin variables help explain cross country differences in creditor rights, enforcement quality, and accounting standards. As shown **by** LLSV **(1998),** English legal tradition countries have laws that emphasize the rights of creditors to a greater degree than the French, German, and Scandinavian countries. French civil law countries protect creditors the least, with German and Scandinavian civil law countries falling in the middle. In terms of enforcement quality, countries with a French legal heritage have the lowest quality of law enforcement, while countries with German and Scandinavian legal traditions tend to be the best at enforcing contracts. Finally, LLSV **(1998)** show that countries with an English legal tradition tend to have much better accounting standards than French or German civil law countries.

Table **7** shows that cross-country differences in creditor rights, enforcement quality, and accounting standards help explain cross-country differences in financial intermediary development, even after controlling for the level of income per capita. Jointly, the variables CREDITOR, **ENFORCE,** and **ACCOUNT** explain a significant amount of the cross-country variation in the three financial intermediary indicators (PRIVATE CREDIT, LIQUID LIABILITIES, and **COMMERCIALCENTRAL** BANK). Each of the legal/accounting indicators, however, is not significantly correlated with all of the intermediary measures. For instance LIQUID LIABILITIES is most closely associated with **ENFORCE** and CREDITOR. In turn, **COMMERCIAL-CENTRAL** BANK and PRIVATE CREDIT are very strongly linked with **ENFORCE** and **ACCOUNT.** The basic message that emerges
- <mark>Second, the “relative inflation” and “real exchange rate” components of automatic debt dynamics in the new template add little by way of policy insight while requiring additional information on the bilateral real exchange rate relative to the US as well as inflation differentials. These two components capture the capital loss from currency depreciation on $-denominated debt and should be amalgamated into the cost of $ debt to guide borrowing decisions, with the “relative inflation” and “real exchange rate” components dropped from the DSA.</mark>

- <mark>Third, the contribution of the composite real interest rate to the change in the debt-to-GDP ratio should be broken down into its local currency and $ components to get a better idea of debt dynamics and to guide borrowing decisions. Notice that the composite real interest rate and its breakdown into its L and $ components embody market signals on default risk and these would be worth an explicit discussion.</mark>

- <mark>Fourth, the DSA should include a few more years of history to help readers without access to the underlying data files. It should also add information on the shares of local and $-denominated debt in total public debt, on the notional nominal interest rates on these two components of debt calculated (as discussed in section III) and currency depreciation based on end-year exchange rates to the “Memo” section of the DSA.</mark>

Table 1 summarizes the suggested changes in the 2023 MAC DSA template.

Table 1: Summary of Suggested Changes in the 2023 MAC DSA Template <!-- Start of picture text --> 2023 MAC DSA Template Suggested Changes<br>Public Debt Public Debt<br>% Foreign ($) ( α )<br> % Local currency ( 1 −α)<br>Automatic Debt Dynamics. Contribution from: Automatic Debt Dynamics. Contribution from:<br>Real interest rate and relative inflation Composite real interest rate ( 1rrcc −1 )<br>Real interest rate<br>+gg ddtt<br> Local currency ( (1 ) 1 LL −1 )<br> Relative inflation rr<br>$ −αα +gg ddtt<br>Real growth rate Foreign ($) ( 1rr ,LL −1 )<br>Real exchange rate Real growth rate ( αα +gg− dd1gtt −1 )<br>+gg ddtt<br>Memo:<br>Memo:<br> Real GDP growth (percent)<br> Inflation (GDP deflator, percent)<br> Inflation (GDP deflator, percent)<br> Nominal GDP growth rate (percent)<br> Nominal GDP growth rate (percent)<br> ( r −g) percentage points<br> Effective interest rate (percent) cc<br> Effective (nominal) interest rate (percent)<br> Local currency ( i )<br> Foreign ($) ( i $ (1 LL + α) )<br>Note: The formulas in the second column come from the various equations in the text above.<br><!-- End of picture text --> <mark>A last set of suggestions pertains to the “realism tools” and risk assessment in the MAC SRDSF, which has two parts: an assessment of near-term stress and one of medium-term risk based on fan charts.</mark> 12
to less energy-intensive, more labor-intensive sectors in the economy (Solaymani et al., 2015). A recent IMF simulation for manufacturing in Asia and the Pacific utilizes national I/O tables to estimate the potential short-term impact of a $25/ton carbon tax (Dabla-Norris et al., 2021). Sectors that rely on carbon-intensive inputs, typically the downstream, extractive and energy-producing industries, are most affected in terms of output and employment. The amount of projected job loss is primarily driven by the industry’s energy dependence and share of employment. This varies substantially across countries. In Malaysia the largest expected job losses are concentrated in electrical and electronics manufacturing while in Mongolia the vast majority projected job losses are in wood and paper manufacturing.

# **3.3.2. Labor targeting climate change policies**

Our review of the literature points to six potential policies to target the impact of climate change on labor. We refer to these as: green jobs, green skills, labor-oriented adaptation, flexible labor regulation, labor market integration, and social protection. As shown in Table 1, each of these relate to one or more of the above discussed potential labor impacts of climate change. In depth or formal evaluation of these policies has so far remained limited. We discuss each in turn.

**Table 1. Labor targeting climate change policies**

|Impact of climate change on labor|Labor oriented policy response<br>|
|---|---|
||H|
|Demand for labor|<br>Green jobs|
|Supply of labor, time use|Labor-oriented adaptation, regulation for flexible work|
|Productivity|Labor-oriented adaptation, regulation for flexible work|
|Self-employed income, vulnerability, poverty|Green jobs, social protection|
|Reallocation of labor|Labor market integration, social protection|
|Skills and human capital|Green skills, social protection, education and health policies|

Policies that promote “green jobs” aim to increase net labor demand while transitioning to a low carbon economy.<sup>lxv</sup> Although comparing the employment impact of green employment policies remains 28
Policy Research Working Paper 4988

# **Abstract**

The debate on whether natural disasters cause significant macroeconomic impacts and indeed hinder development is ongoing. Most analyses along these lines have focused on impacts on gross domestic product. This paper looks beyond this standard national accounting aggregate, and examines whether traditional and alternative national savings measures combined with adjustments for the destruction of capital stocks may contribute to better explaining post-disaster changes in welfare as measured by changes in consumption expenditure. The author concludes that including disaster asset losses may help to better explain variations in post-disaster consumption, albeit almost exclusively for the group of low-income countries. The observed effect is rather small and in the range of a few percent of the explained variation. For low-income countries, capital stock and changes therein, such as forced by disaster shocks, seem to play a more important role than for higher-income economies, where human capital and technological progress become crucial. There are important data constraints and uncertainties, particularly regarding the quality of disaster loss data and the shares of capital stock losses therein. Another important challenge potentially biasing the results is the lack of data on alternative savings measures for many disaster-exposed lower-income countries and small island states.

This paper—a product of the Global Facility for Disaster Reduction and Recovery Unit, Sustainable Development Network—is part of a larger effort in Network to disseminate the emerging findings of the forthcoming joint World Bank-United Nations’ Assessment of the Economics of Disaster Risk Reduction. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The author may be contacted at mechler@iiasa.ac.at.

_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
# **Japan's Trade in Auto Parts Japan's Trade in Auto Parts**

<!-- Start of picture text --> 40,000,000,000 Japan's Trade in Auto Parts<br>37,500,000,00040,000,000,000<br>35,000,000,00037,500,000,000<br>32,500,000,00035,000,000,000<br>30,000,000,00032,500,000,000<br>27,500,000,00030,000,000,000<br>25,000,000,00027,500,000,000<br>22,500,000,00025,000,000,000<br>20,000,000,00022,500,000,000<br>17,500,000,00020,000,000,000<br>15,000,000,00017,500,000,000<br>12,500,000,00015,000,000,000<br>10,000,000,00012,500,000,000<br>10,000,000,0007,500,000,000<br>5,000,000,0007,500,000,000<br>2,500,000,0005,000,000,000<br>2,500,000,000 Imports 0<br>Exports 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003<br>Imports 0<br>Exports 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003<br>Source for this and the following graphs: UN Comtrade Data accessible at<br>http://unstats.un.org/unsd/comtrade/default.aspx<br><!-- End of picture text --> The Korean auto industry grew impressively after making the breakthrough to mass production in the mid-1980s. The financial crisis of 1997-98 dealt a sharp blow to the auto industry. Domestic demand and production recovered sharply over the next couple of years, and then stagnated after 2000, leaving the Korean industry increasingly reliant upon exports and overseas production, principally in the United States and China.

After the depreciation of the won caused by the financial crisis, parts exports began to catch up with the growth of vehicle exports, and Korea started running significant trade surpluses in parts. Korea's exports of auto parts, however, are still only about one-eighth of the value of those of Japan. Inward foreign investment leaped in the two years following the crisis before levelling off again, while outward investments by parts makers increased from a low base.

8