rafmacalaba/datause-encoder-data huggingface dataset:
Table 3: Impacts on households during July 2005 extreme event

||**K East**|**H East**|**F North**|**F South**|**L Ward**|**P North**|
|---|---|---|---|---|---|---|
||**(Figures**<br>|**in bracket**<br>|**as % of aver**<br>|**age househ**<br>|**old monthl**<br>|**y income)**<br>|
|Income loss due to floods|10474<br>(69.8)|8543<br>(57.0)|5164<br>(25.8)|8323<br>(41.6)|22578<br>(112.9)|14894<br>(74.5)|
|Amount spent on|22270|26191|34335|42967|22457|27118|
|repair/rebuilding of<br>house/premises|(148.5)|(174.6)|(171.7)|(214.8)|(112.3)|(135.6)|
|Losses due to damage to|13190|15469|13442|10081|11325|23923|
|household appliances<br>(TV, refrigerator, washing<br>machine, etc.)|(87.9)|(103.1)|(67.2)|(50.4)|(56.6)|(119.6)|
|Losses on account of damage|9735|11061|11756|6602|7121|10417|
|to household assets (Furniture<br>and utensils)|(64.9)|(73.7)|(58.8)|(33.0)|(35.6)|(52.1)|
|Losses due to damages to|12974|9153|11833|1250|5478|7232|
|vehicles<br>(Car,Motorcycle,Bicycle)|(86.5)|(61.0)|(59.2)|(6.3)|(27.4)|(36.2)|

Source: Patankar et al., 2012 Table 4 further looks at these impacts across different income categories. Similar income classification of BPL, LIG, MIG and Higher MIG is used here depending on the income ranges originally used in the study. The table shows how the poor and low income houses suffered relatively far greater magnitude of losses compared to their earnings. For instance, BPL houses suffered losses of Rs. 37,000 on average, which is more than six times their monthly income. But for LIG families, losses are 4‐5 times their monthly income, thrice for the MIG households and twice for the higher MIG families. Hence, the poor and low income households with limited or no ability to bear the losses have suffered more during the extreme floods compared to their better‐off counterparts.

Table 4: Impacts distributed across income categories (2005 floods)

|**Category**|**Income**<br>**loss**|**Damage to**<br>**appliances**|**Damage to**<br>**assets**|**Amount**<br>**spent on**<br>**house repairs**|**Damage**<br>**to**<br>**vehicles**|**Average**<br>**losses**|
|---|---|---|---|---|---|---|
|Less than 5000 (BPL)|5000|11000|6700|15000|6500|37000|
|5000‐15000 (LIG)|5000|13000|7000|22000|9000|48000|
|15000‐30000 (MIG)|14000|14000|10000|30000|10000|57000|
|Above 30000 (Higher<br>MIG)|12000|17000|13000|45000|‐|69000|

Source: Calculations based on data obtained from 1168 households 16
_81_ _Annex C Expert Assesments_ rate is credited, the nonreportng countries would need a rate of 540-somewhat too high to accept-to produce the WHO (1986) figure. Similarly, for Asia, reporting countries (among which we count China and rural India) have a rate of 202, and a rate of 1048 would be needed in the nonreporting countries to produce the WHO (1986) figure.

Adopting the WHO (1986) estimates, and applying age distributions from the regression estimates, would raise deaths due to complications of pregnancy from 218 to 492 thousand in developing countries, an increase of 130 percent.

## _Diabetes_

For diabetes, the recommendations were essentially subjective: to replace SubSaharan African rates with rates lower than developed country rates; to reduce Asian rates to reported levels for Korea, Singapore, Thailand, and Hong Kong, treating these as setting an upper bound; to reduce rates for Latin America somewhat; to eliminate deaths at age 0; and to allow a nominal number of deaths, say 1 in 100,000, at age 5-14.

The revised African rates were adopted. The Asian rates were not changed because calculations subsequent to those provided to the expert had already reduced them. Latin American rates were not changed for various reasons, including the suspicion that there may be higher rates in the Caibbean. Deaths at age 0 were not eliminated because such deaths were in fact reported. And increasing the rate at 5-14 was not thought necessary because the regression estimates are in fact nonzero, though quite small.

The result of changing the Afdcan rates is to reduce world mortality rates slightly from 10.5 to 9.9 and deaths from 507,000 to 477,000.

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# **Evolving Wage Cyclicality in Latin America**

Julián Messina World Bank, Office of the Chief Economist for LAC and IZA Luca Gambetti<sup>1</sup> Universitat Autònoma de Barcelona and Barcelona GSE Keywords: Downward Wage Rigidity, Indexation, Real Wage Cyclicality, Vector Autoregression, Time Varying Coefficients, Bayesian Estimation JEL Classification: E24.

1 We thank Bill Maloney, Daniel Lederman and seminar participants at the 2013 Latin American Economic Association Meetings for their helpful comments. Luca Gambetti acknowledges the financial support from the Spanish Ministry of Science and Innovation through grant ECO2009-09847 and the Barcelona Graduate School Research Network.
_Table 19. Overview of disaster loss information availability in the LSMS-ISA microdata_

|Country|Year|Ability to quantify<br>partial losses|List of disaster shocks<br>options provided in the<br>dataset|
|---|---|---|---|
||2011-2012|No|Dht Ri Fi|
|Ethioia|2013-2014|Yes|roug; ans: re;<br>Insects Cro disease|
|p|2015-2016|Yes|; p ;<br>Locusts Hail|
||2018-2019|Yes|;|
||2009-2010|No|Dht Il i|
|Mli|2012-2013|No|roug; rreguar rans;<br>Fld t i 1 Fi|
|aaw|2015-2016|No|oos (no n wave ); re;<br>Insects Disease|
||2018-2019|Yes|;|
|Mli|2014|Yes|Drought; Rains; Fire;|
|a|2017|Yes|Insects;Disease|
|Niger|2011|Yes|Insects and bird attacks;<br>|
||2014|Yes|Plant illness; Drought; Flood|
|Nigeria|2019|Yes|Drought; Flood; Pest;|
||2008|No||
||2010|No||
|Tanzania|2012|No|Drought; Rain; Fire; Insects|
||2014|No||
||2019|No||

41
# **3.4 Resources and Expenditures**

The average budget of EPAs surveyed is around 0.11 percent of the value of exports of goods and services, with a standard deviation of 0.35 and a median of 0.04 percent. The region with the largest average budget is LAC at 0.17 percent of exports. It is followed by EEA at 0.12 percent, and then MENA, SSA, and the OECD with average budgets of around 0.09 to 0.10 percent of exports. The distribution within regions varies as shown in Figure 4. The OECD has the bulk of its agencies’ budget between 0.03 and 0.13 percent of exports (agencies within the 25th and 75th percentile). In SSA, the bulk of the agencies is between 0.00 and 0.05; the large mean is explained by the fact that a few countries have extremely large budgets relative to exports, often supported by multilateral and bilateral donors.

Regarding funding sources, around 52 percent of the agencies obtained more than 75 percent of their budget from public funding; 2 percent of the agencies obtained more than 75 percent of their budget from private funding; 3 percent of the agencies obtained more than 75 percent of their budget from selling their services (customer fees); and 2 percent of the agencies obtained more than 75 percent of their budget from either multilateral or bilateral donors. Thus, public funding seems to predominate as a source of funding. Three quarters of the agencies surveyed had no private funding, and half had no income associated with the selling of their services. When they reported some income, it represented on average less than 10 percent of their budget. The importance of public funding varies across regions as illustrated in Figure 5. The region where public funding may be less predominant is Latin America, where only 35 percent of the agencies reported that public funding represented more than 75 percent of their total budget. The largest share of agencies funded at more than 75 percent by public funding is found in EEA.

# **3.5 Activities and client orientation**

We considered four main activities: 1) country image building (advertising, promotional events, but also advocacy); 2) export support services (exporter training, technical assistance, capacity building, including regulatory compliance, information on trade finance, logis10
# **Figure 14. Backward and Forward Integration to Manufacturing GVCs by Sources and Destinations of Imports and Exports of Intermediate Inputs: China (1995 vs. 2015)**

<!-- Start of picture text --> 100.0<br>90.0<br>80.0<br>70.0 73.5<br>60.0 61.2<br>54.3<br>50.0<br>40.0<br>30.0<br>20.0<br>14.4<br>10.0<br>0.0 0.7 1.5 0.2 0.6<br>-10.0 West East Asia Benchmark NRR oil NRR Non- Resource Sub India USA Germany<br>Europe Exporters Oil Poor Saharan<br>Exporters Africa<br>Backward Integration (2015) Forward Integration (2015)<br>Δ GVC (2015/1995) Δ Backward Integration (2015/1995)<br>Δ Forward Integration (2015/1995) GVC (2015)<br><!-- End of picture text --> _Source_ : Authors computation using data from EORA database.

**Figure 15. Percentage Rates of Backward and Forward Integration to Manufacturing GVCs by Sources and Destination of Imports and Exports of Intermediate Inputs: Western Europe (1995 vs. 2015)**

<!-- Start of picture text --> 100.00<br>90.00<br>87.1<br>80.00<br>70.00<br>60.00<br>50.00<br>40.00<br>33.7<br>30.00<br>25.3<br>20.00<br>10.00 8.0<br>0.00 1.9 1.0 0.5 0.4 0.1 0.2<br>-10.00 East Asia Benchmark NRR oil NRR Non- Resource Sub China India USA Germany<br>-20.00 Exporters Oil Poor Saharan<br>Exporters Africa<br>Backward Integration (2015) Forward Integration (2015)<br>Δ GVC (2015/1995) Δ Backward Integration (2015/1995)<br>Δ Forward Integration (2015/1995) GVC (2015)<br><!-- End of picture text --> _Source_ : Authors computation using data from EORA database.

33
lished by the national statistical agencies of the CIS-7. We review the statistics first and then we proceed with a multivariate analysis using the BEEPS database. Possible barriers to growth and job creation are grouped into four categories: Economic barriers (internal and external demand, competition, predictability of changes in policies and legislation, infrastructure), financial barriers (finance, banking, interest rates, taxes), institutional barriers (governance, corruption, customs, judiciary and others) and internal barriers to the enterprise (ownership, management and workers’ skills).

# **Economic barriers**

_Internal demand._ One measure of internal demand is final consumption expenditure. Figure 3.1 compares final consumption expenditure as % of GDP for the three groups of countries, CIS-7, Other CIS and Non CIS. The share of internal demand over GDP is generally very high for the CIS-7, in a range comprised between 70% and 90% of GDP and higher than in other transitional economies. This is partly a reflection of the large informal sector in these countries. In 1994 in Georgia, final consumption expenditure reached a figure of over 140% of GDP. It is also evident that internal demand in the CIS-7 declined significantly in the aftermath of the 1998 Russian financial crisis and has yet to recover.

_External demand._ The devaluation of the national currencies that followed the 1998 Russian financial crisis has instead prompted a positive growth path in external demand. The share of exports over GDP have increased between 1998 and 2000 and then stabilised around 40% of GDP in the subsequent period.

In sum, the key to understanding demand for goods and services during the recent growth period in the CIS-7 is the 1998 Russian financial crisis. This has affected negatively internal demand (about -10% of GDP) and positively external demand (about +10% of GDP). Thus, the two effects have roughly cancelled each other out.

**Figure 3.1 Internal and External Demand**

**Final Consumption expenditure (% of GDP) Exports (% of GDP)**

<!-- Start of picture text --> 110 65.0<br>105 60.0<br>100<br>55.0<br>95<br>90 50.0<br>CEE CEE<br>85 CIS non CIS-7 45.0 CIS non CIS-7<br>CIS-7 CIS-7<br>80 40.0<br>75<br>35.0<br>70<br>65 30.0<br>60 25.0<br>1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 1998 1999 2000 2001 2002<br><!-- End of picture text --> Source: http://www.cisstat.com _Competition_ . Despite improvements in exports, local firms at the end of the 1990s were still relatively little exposed to world competition. For most enterprises in the CIS-7, competition was feared from domestic enterprises, SMEs above all. According to the BEEPS, the number of enterprises that ranked domestic firms as the main competitors was similar across transitional economies with the exception of Armenia where this share was almost twice as large as elsewhere perhaps suggesting that SMEs in this country are much more active than in other countries. Firms in Uzbekistan also ranked competition from large private and state owned enterprises much more 18
Policy Research Working Paper 10065

# **Abstract**

The information contained in the Enterprise Survey— administered by the World Bank (WB), the European Bank for Reconstruction and Development (EBRD), and the European Investment Bank (EIB)—is used to build a firm-level “Corporate Environmental, Social, and Governance Responsibility” composite indicator. The novelty of the indicator, compared with the corporate social responsibility scores and environmental, social, and governance (ESG) scores already available, is due to its unique coverage, namely, a large number of private sector small and medium-size enterprises in selected emerging economies (more than 40 economies covered by the Enterprise Survey). The composite indicator summarizes information on private sector actions on environmental, social, and governance factors. The analysis shows that the actions of private sector small and medium-size enterprises in emerging economies to foster sustainability and green growth significantly lag in the transition to a more sustainable business environment, and large gaps persist. Among emerging economies, those in the Middle East and North Africa—which deserve special attention due to the urgent need to green their growth model—are among the worst performers. Larger companies in the Middle East and North Africa show better environmental, social, and governance performance than small and medium-size enterprises in other areas; but smaller firms in the Middle East and North Africa show extremely weak performance in many aspects, even if controlling for the relative level of economic development. The weakness of environmental, social, and governance practices among firms in the Middle East and North Africa is due to the social (with large gaps in female participation in the workforce and management) and environmental topics. This calls for urgent policy action to address such weaknesses and exploit the full potential of the region.

This paper is a product of the Office of the Chief Economist, Middle East and North Africa Region. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/prwp. The authors may be contacted at m.ferrazzi@eib.org and a.tueske@eib.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
Transition economies represent an upper bound. Their initial returns to education were abnormally low, due to the deliberate earnings compression pursued by socialist regimes. For most developing countries confronting globalization, the initial wage inequality by skill is much higher. Still, a more systematic analysis of the relationship between openness indicators and returns to education confirms that globalization does increase the wage gap between skilled and unskilled workers, at least in the short run. Figure 6 shows the effect of increased openness on the wage premium to an additional year of schooling. This effect is estimated using data from more than three hundred studies on the returns to education in developing countries. Again, those studies use data sets with different coverage and do not necessarily rely on the same econometric approach. But as long as the nature of the data and approach are not affected by openness, this diversity should not bias the results.

The bars in Figure 6 indicate point estimates for this effect, based on regressions that allow for different returns by country and year. These regressions include as the key explanatory variable an openness indicator, lagged one year. For the bar on the left, this indicator is the ratio of trade to GDP; for the bar on the right, it is the ratio of foreign direct investment to GDP. The estimated effects are sizeable. An extra one percent of GDP in foreign direct investment raises the premium associated with an additional year of education by almost 0.8 percentage points. This is equivalent to increasing the wage gap between a college-educated person and a worker with primary education by roughly 10 percentage points. Moreover, the estimated effects are statistically significant and their size does not change much if other control variables are added to the regression, or a contemporary openness indicator is considered.

> **Figure 6. Returns to Education and Globalization**

<!-- Start of picture text --> 0.9<br>0.8 -<br>0.5 -<br>K 0.4<br>0.3-<br>02 02<br>0.1 -<br>0.0 -_______,<br>Extra 10% of GDP in trade<br><!-- End of picture text --> Extra **1%** of GDP in **FDI** _Source:_ Based on Ramna (2001b).