rafmacalaba/datause-encoder-data huggingface dataset:
lower. Survival rates of new entrants into export markets exhibit no clear correlation with the level of development. One possibility is that survival rates may depend more heavily on characteristics of the primary destination markets (neighborhood) than of the source markets. Indeed, the three countries with the lowest survival rates are in Africa, and two are landlocked. Table 4 shows that all the correlations discussed so far, with the exception of survival rates, are highly significant.

**Table 4: Correlations between Database Measures, GDP per Capita, GDP, and the Ratio of**

**Exports to GDP**

||**Ln Number of**<br>**Exporters**|**Ln Mean**<br>**Exports per**<br>**Exporter**<br>**(mn USD)**|<br>**Share of Top**<br>**5% Exporters**|**Ln Number of**<br>**Products per**<br>**Exporter**|<br>**Ln Number of**<br>**Destinations**<br>**per Exporter**|**Entry**<br>**Rate**|<br>**Exit**<br>**Rate**|<br>**Survival**<br>**Rate**|
|---|---|---|---|---|---|---|---|---|
|**Ln GDP per capita**|0.72|0.35|0.32|0.40|0.38|-0.32|-0.25|-0.02|
||(0.00)|(0.02)|(0.03)|(0.01)|(0.01)|(0.04)|(0.10)|(0.90)|
|**Ln GDP**|0.90|0.52|0.15|0.41|0.54|-0.48|-0.35|0.16|
||(0.00)|(0.00)|(0.31)|(0.01)|(0.00)|(0.00)|(0.02)|(0.34)|
|**Ratio of exports to GDP**|0.27|0.53|0.08|0.45|0.43|-0.32|-0.32|-0.03|
||(0.08)|(0.00)|(0.59)|(0.00)|(0.00)|(0.04)|(0.04)|(0.84)|

Notes: P-value shown in parentheses. The measures used in the correlations are based on measures in the countryyear level dataset (file CY.dta) averaged across the 2006-2008 period for each country for each country except Kuwait for which averages are across the period 2009-2010 and Portugal for which averages are across the 20032005 period.

Table 4 and the figures in Appendix 3 show that the results are quite similar when the correlations are established with either the economic size of countries or the importance of their export sector instead of their level of development. In terms of magnitudes, the correlations of most measures tend to be higher with economic size than with the level of development or the ratio of exports to GDP.<sup>39</sup>

> 39 Kuwait and Portugal are part of the sample used to compute the correlations in Table 4 despite their data covering a period other than 2006-2008. However, similar correlation patterns are obtained if they are excluded from the sample.

25
# **Household Coping and Response to Government Stimulus in an Economic Crisis: Evidence from Thailand**

**Shahidur R. Khandker** World Bank, Washington DC <u>skhandker@worldbank.org</u>

**Gayatri B. Koolwal** World Bank, Washington DC <u>gkoolwal@worldbank.org</u>

## **Jonathan Haughton**

Suffolk University, Boston <u>jonathan.haughton@suffolk.edu</u>

**Somchai Jitsuchon** Thailand Development Research Institute <u>sj.tdri@gmail.com</u> **March, 2012**

JEL Codes: E21, H12, I38 Keywords: Economic Crisis, Fiscal Stimulus, Consumption Smoothing, Thailand We would like to thank Will Martin and James Seward for valuable feedback and comments, and Sanonoi Buracharoen of the National Statistics Office for help with understanding the data.
- 11 -

where _C_ denotes total operating costs,<sup>_Y_</sup> _i_ is _i_ th output, and _Wk_ is _k_ th input price. _Z m_ represents an exogenous technical variable, such as the network size, to control for heterogeneity among utilities.

Following Garcia and Thomas (2001) and García-Valiñas and Muñiz (2007), two output variables are adopted: the amount of water sold and the amount of sewage water collected. These are denoted by _Yw_ and _Ys_ , respectively. Our database covers both water and sewerage utilities. Naturally, some water utilities are providing sewerage services as well. If an operator supplies either water or sewerage service, the nonoperational output measure is set at a very small positive number, but not zero.

Two inputs are conceptually considered: labor and other operation and maintenance (O&M) materials. Unit labor price _WL_ is obtained by dividing total wage expenses by total number of employees (in both water and sewerage operations). On the other hand, O&M material expenses potentially consist of various costs, and the unit price of input materials ( _WM_ ) is computed by dividing the operating expenses other than wages by the sum of the two output variables. This is just for computational purposes.

Two technical characteristics are involved: the number of water connections denoted by _ZW_ , and the number of sewage connections, i.e., _ZS_ . These are supposed to capture the degree of economies of customer density. In the utility sector, it may be possible to increase service production with its network size unchanged, yielding higher profits. Even with some
countries (World Bank, 1989). In any case, the result has been groupings that have contained sufficiently large numbers of countries to be analytically useful. The chosen levels have occasionally been hotly debated, but it is likely that this would occur in any case, regardless of the conceptual underpinning.

An equally important discussion as the original choice of threshold levels is the manner in which they should be adjusted over time. The underlying objective has been to maintain their value in constant prices, so a method has been needed to account for inflation. Initially, the thresholds used in the operational guidelines were updated using the US GNP deflator, but this was changed to the SDR deflator, beginning with the 1982 data (World Bank, 1983b). The analytical income classification follows the same methodology and uses the SDR deflator to maintain the thresholds constant in real terms. One way to think of this is to suppose that thresholds are really set in terms of SDRs, but converted to US dollars for presentation purposes.

Is the SDR deflator is an appropriate measure of inflation in the context of updating the thresholds? Other choices have certainly been considered. For example, the same methodology paper that resulted in the use of the SDR deflator (World Bank, 1983b) also proposed that a more appropriate measure would be the average inflation of countries close to each threshold.

The SDR deflator is essentially a measure of the average inflation (measured by the GNP deflator) of the countries whose currencies make up the SDR: the Euro Area, the US, the UK, and Japan. The SDR deflator is calculated by weighting the inflation rates of the countries that contribute to the SDR basket of currencies according to their weight in the SDR;<sup>13</sup> the IMF calculates the value of the SDR for a five-year period (Table 5).

> 13 See https://datahelpdesk.worldbank.org/knowledgebase/articles/378829‐what‐is‐the‐sdr‐deflator.

23
district-level panel on the number of branches by bank (see Allen, et al, for details). The panel covers 45 commercial banks that operated in 65 Kenyan districts in the period between 2006 and 2009.<sup>28</sup> This new dataset allowed the authors to explore the relationship between bank expansion and access to banking services over time and to exploit within-district variation in bank presence. The household-level data is from the FinAccess surveys conducted by Financial Sector Deepening Trust Kenya (FSD Kenya) in 2006 and 2009. The 2006 survey consisted of 4,420 completed interviews, while the 2009 survey consisted of 6,598 completed interviews.

Allen et al. (2011) find that the presence of domestic private banks, government banks or foreign banks has a positive impact on access to bank accounts at the district level. Interestingly they also find that the presence of Equity Bank in a specific district is strongly positively related to the residents’ probability of having a bank account and/or a bank loan, which goes beyond the effect of bank branch expansion and presence of other commercial banks in Kenya. These results are consistent with the stylized facts that we discussed above, and they highlight the importance of institutions, such as Equity Bank, with a business model focused on the provision of financial services to population segments ignored by traditional commercial banks while generating sustainable profits in the process. Such institutions can be an important solution to the financial access problem that has hindered the development of inclusive financial sectors in many developing countries.

# **VI. Conclusions**

The available evidence provides a convincing linkage between financial development and economic development. Yet the level of financial development remains very low in Africa based

> 28 Although Kenya is currently divided into 46 districts, the data set exploits a more disaggregated country division as of the 1999 census. In the 1999 census, Kenya comprised eight provinces (Central Coast, Eastern, Nairobi, North Eastern, Nyanza, Rift Valley, and Western) that were subdivided into 69 districts. Of these 69 districts, the survey data consider covers 65.

37
<!-- Start of picture text --> Marginal Effects of Land Size on Teff<br>Yield<br>Smallholders Commercial Farms<br>.4 .4<br>.2 .2<br>0 0<br>-.2 -.2<br>-.4 -.4<br>-2.63 -1.78 -1.19 -0.64 0.02 -0.22 1.09 1.67 2.19 2.89<br>Incidence of Fertilizer Use<br>Smallholders Commercial Farms<br>.2 .2<br>.1 .1<br>0 0<br>-.1 -.1<br>-.2 -.2<br>-2.63 -1.78 -1.19 -0.64 0.02 -0.22 1.09 1.67 2.19 2.89<br><!-- End of picture text -->

**Figure 4: Marginal effects of land size on teff**

<!-- Start of picture text --> Marginal Effects of Land Size on Wheat<br>Yield<br>Smallholders Commercial Farms<br>.4 .4<br>.2 .2<br>0 0<br>-.2 -.2<br>-.4 -.4<br>-.6 -.6<br>-3.47 -2.27 -1.64 -1.06 -0.35 -0.13 1.87 3.40 4.91 6.25<br>Incidence of Fertilizer Use<br>Smallholders Commercial Farms<br>.15 .15<br>.1 .1<br>.05 .05<br>0 0<br>-.05 -.05<br>-.1 -.1<br>-3.47 -2.27 -1.64 -1.06 -0.35 -0.13 1.87 3.40 4.91 6.25<br><!-- End of picture text -->

**Figure 5: Marginal effects of land size on wheat**

25
THE IMPACT OF INFRASTRUCTURE SPENDING IN SUB-SAHARAN AFRICA

# **Benin > Electricity investments > Macro results**

||||Reduction in<br>other ublic|El|ectricity<br>Imort|Forein|Income|
|---|---|---|---|---|---|---|---|
|Variable|Definition|Reference|p<br>expenditure|VAT|p<br>duties|g<br>aid|<br>tax|
|Ym|Aggregate household income|1,250,000.00|0.95|0.70|0.70|0.72|0.72|
|EV|Equivalent variation||0.93|0.62|0.64|0.70|–1.56|
|s|Wage|1.00|12.88|4.18|4.52|4.81|5.30|
|yg|Government income|214,000.00|25.13|13.97|13.97|13.97|13.97|
|g|Total government expenditure|132,000.00|28.93|10.77|10.77|10.77|10.77|
|ye|Firm income|135,000.00|–1.36|–0.04|–0.10|–0.14|–0.22|
|It|Total private investment|463,000.00|–0.45|0.61|0.42|0.11|–1.22|
|GDP|GDP|1,320,000.00|0.54|0.64|0.64|0.64|0.64|
|e|Nominal exchange rate|1.00|–11.79|1.43|–1.13|–5.44|0.41|

# **Benin > Electricity investments > Sectoral results**

||||Reduction in|El|ectricity|||
|---|---|---|---|---|---|---|---|
||||other ublic||Imort|Forein|Income|
|Variable|Sector|Base|p<br>expenditure|VAT|p<br>duties|g<br>aid|<br>tax|
||Crop agriculture|461,000.00|–0.31|–0.06|–0.09|–0.09|–0.15|
|Va|Export agriculture|95,000.00|–0.04|0.24|0.19|0.22|0.17|
|Value added or|Industries|190,000.00|0.77|1.18|0.86|1.51|1.09|
|output|Private services|477,000.00|–2.25|–0.05|0.12|–0.28|0.11|
||Public services|93,400.00|19.10|6.96|6.86|7.58|6.83|
||Crop agriculture|1.01|0.05|1.22|1.19|0.59|–0.83|
||Export agriculture|1.00|0.08|2.60|1.59|2.11|0.80|
|pq<br>Market rices|Industries|1.10|–5.21|1.77|2.56|–2.35|0.09|
|p|Private services|1.06|–0.42|2.37|0.30|–0.59|–0.66|
||Public services|1.00|8.25|3.56|3.66|2.96|3.69|
||Crop agriculture|1.00|0.04|1.12|0.61|0.64|–1.01|
|r<br>|Export agriculture|1.00|0.19|2.92|1.25|2.72|1.12|
|Rental rate of<br>capital|Industries|1.00|6.03|0.21|–1.28|2.78|0.80|
||Private services|1.00|–7.12|–2.17|–0.80|–2.92|–0.17|

30
estimates of years of schooling of the cohort of 25- to 29-year-olds for S�, Schoellman’s estimates of labor market returns for Q�, and his preferred value of 0.5 for η. The right panel of Figure 5.7 shows that there is broad agreement between the two measures (excluding just three outliers, the correlation coefficient between the two measures is 0.84, and the rank correlation is 0.85).

# **_5.3 Quality adjustments based on grade-to-grade test-scores increases_**

Third, we can test the plausibility of _LAYS_ by comparing its findings with those of other efforts to adjust schooling for quality. Beyond _LAYS_ and Schoellman’s returns-based approach, there are alternative ways to adjust years of schooling for quality to allow cross-country comparison. One relevant recent study that (like _LAYS_ ) relies on measured test scores to do this is Kaarsens (2014), which derives measures of education quality across countries using multiple rounds of TIMSS (1995-2011). The analysis exploits the fact that in 1995, students were tested in Grades 3 and 4 at the primary level and at Grades 7 and 8 at the secondary level.<sup>24</sup> It uses these data to calibrate a model that simultaneously estimates overall average annual test score growth (that is, the average changes from Grades 3 to 4, and from 7 to 8), along with “education quality,” which is (essentially) a country-specific fixed effect calculated after controlling for the overall average test-score growth.<sup>25</sup> The approach then normalizes the quality measure relative to the United States. To more directly compare the approach to the _LAYS_ approach, we renormalize the “education quality” estimates so they are expressed relative to Singapore’s; we then recalculate _LAYS_ by multiplying that relative quality measure by the Barro-Lee average years of schooling of the cohort of 25- to 29-year-olds.

The left panel of Figure 5.8 compares Kaarsen’s (renormalized) education quality variable to the measure of learning per year relative to Singapore that we use (that is, the data underpinning Figure 2.1); the right panel compares the _LAYS_ estimates that follow from the two approaches. Kaarsen’s approach suggests that there are larger differences across countries in

> 24 The data for Grades 3 and 7 were made available to him as a result of personal communication with the IEA, the organization that produces TIMSS.

> 25 According to Kaarsen’s calculations, the average TIMSS test-score gain in math at the secondary level is 29.4 points, and in science at the secondary level it is 36.5 points.

37
# Introduction

The most immediate consequences of floods are the fatalities and casualties and the first priority of disaster risk management is to save lives. But natural disasters also have economic consequences, which affect wellbeing and need to be accounted for and managed (Cavallo and Noy, 2011; Rose, 2009; Skoufias, 2003). These economic consequences and wellbeing losses depend on the value of what is lost or damaged, and on many other factors, including how long it takes to rebuild, how asset losses translate into income losses, and how coping mechanisms and ex‐post support (from insurance to social protection) protect the victims and help smooth consumption losses (Carter et al., 2007; Le De et al., 2013). In addition, wellbeing losses are larger when losses are concentrated, especially when concentrated on poor people – as is often the case (Hallegatte et al., 2016a). Disasters can also have irreversible and long‐term health consequences, particularly on children (Dercon, 2004; Maccini and Yang, 2009).

Many policies can minimize wellbeing losses and protect the population: from building dikes and restoring mangroves to better land‐use planning and early warning, to evacuation, insurance and social safety nets. Risk management policies are best designed as holistic strategies that combine many of these levers (World Bank, 2013).

Designing such a consistent policy package is challenging; here we propose an approach and a model to support this process. In this work‐in‐progress, we combine data on flood hazard, population and asset location, asset vulnerability, and socioeconomic characteristics and combine insights from natural and social sciences to assess how floods affect wellbeing, measured using a _social welfare function_ ( _welfare_ is the metric used by economists to measure wellbeing). We present the model using a case study – the 2005 floods in Mumbai, India, for which macro‐ and micro‐economic data are available. We then use the model to define and assess the socioeconomic resilience of 90 countries to river floods, identify policy priorities to reduce the impact of floods on wellbeing, and help design holistic risk management strategies tailored to each country.

An Online Technical Note<sup>1</sup> provides all the equations and technical details of the model, sensitivity analysis to show the robustness of the approach, and a comparison with other indicators for resilience or vulnerability. The model and the data are also available online.<sup>2</sup> We propose a new quantifiable definition of _socioeconomic resilience_ , the ratio of _asset_ losses to _welfare_ losses<sup>3</sup> : Socioeconomic resilience �<sup>Asset losses</sup> Welfare losses

> 1 The online technical note is available at

<u>http://pubdocs.worldbank.org/en/362361462825935820/pdf/Online‐Technical‐Note‐submit.pdf</u> 2 The code and data set are available at github.com/adrivsh/resilience_indicator_public/

> 3 This definition combines the previously proposed notions of macroeconomic and microeconomic resilience (Hallegatte, 2014).

2
# **TABLE 8. HEALTH WORKER ABSENTEEISM SELECTED COUNTRIES, 1996-2004**

<!-- Start of picture text --> Dominical Republic (1996)* Physicians<br>Physicians<br>Bangladesh (2004)<br>clinics<br>Bangladesh (2004) Physicians<br>Bangladesh (2004) Health staff<br>Uganda (1997) Rural physicians<br>Uganda (2002/03) Health staff<br>Peru (2002/03) Health staff<br>Absentee rate (%)<br>Indonesia (2002/03) Health staff<br>India (Udaipur dist.) (2004) Health staff rural clinics<br>India (2002/03) Health staff<br>Honduras (2001) Health staff<br>Chad (2004) Health staff<br>Cameroon (2003) Health staff<br>Bangladesh (2002) Health staff<br>0 10 20 30 40 50 60 70 80<br><!-- End of picture text --> Note: *Santo Domingo Hospital.

Sources: Chaudhury et al. (2006); Chaudhury and Hammer (2005); World Bank (2001); Gauthier (2006); Lewis, La Forgia, and Sulvetta (1996); McPake et al. (1999); and Banerjee, Deaton, and Duflo (2004).

34
has also questioned whether life expectancy is a good indicator of the quality of life; Sen (1985, p.30) notes that ―The quality of life has typically been judged by such factors as longevity, which is perhaps best seen as reflecting the quantity (rather than quality) of life.‖ Possibly it is the combination of GDP and life expectancy that somehow captures ―capabilities,‖ but then where in Sen‘s writings do we find guidance on the valuation of life, as required by any (positively weighted) aggregation function defined on income and life expectancy? (I return to the issue of tradeoffs below.) It is clearly a large step indeed from Sen‘s (often powerful) theoretical insights to the idea of ―human development‖ found in the HDRs, and an even bigger step to the specific measure that is the HDI.

A similar comment applies to the MPI. In defending their data and methodological choices, the authors of the MPI contrast their index to poverty measures based on consumption or income, arguing that ―the MPI captures direct failures in functionings that Amartya Sen argues should form the focal space for describing and reducing poverty‖ (Alkire and Santos, 2010a, p.1). However, the various components of the MPI include measures of deprivation in the attainments space as well as functionings. As with the HDI, it is unclear how much this really owes to Sen. And if one looks at how poverty lines are in fact constructed for most conventional poverty measures found in practice, they too can claim no less credible antecedents in Sen‘s approach. By this interpretation, the poverty line is the monetary cost of attaining certain basic functionings, as outlined in Ravallion (2008). In practice, the main functioning is adequate nutritional intakes for good health and normal activities, though an allowance for basic non-food needs is almost always included. More generally one can define a poverty line as a money metric of welfare. By normalizing consumption or income by such a poverty line,<sup>12</sup> the resulting poverty measure comes to reflect something closer to the broader concept of welfare than the authors of the MPI appear to have in mind. The key point here is that doing analysis in the income space does not preclude welfare being defined in other spaces, as has long been recognized in economics.

> discuss specifics of how GDP per capita enters the HDI. (The income variable switched to Gross National Income in the 2010 HDR.)

> 12   Blackorby and Donaldson (1987) call these ―welfare ratios‖ and show that aggregating empirical money-metric welfare (―equivalent income‖) functions into empirical social welfare functions can be problematic unless the money metric of utility can be written as a welfare ratio.

10
Assets Limited” (100% stake). <mark>In April 2010 Rosgosstrakh became 100% shareholder of R</mark> osgosstrakh – <mark>Armenia. h</mark> <u>ttp://www.armbanks.am/en/insurance-market/?org=61&lang=en, www.rgs.am</u> 12. “Sil Insurance" was registered in March 1, 2000. Shareholders are Sukiasyans family (4 shareholders, Armenia) with 80% stake and "Armeconombank" Ltd. <u>http://www.armbanks.am/en/insurance-market/?org=83&lang=en</u> _<u>,</u>_ <u>www.silinsurance.am</u>

# **III. Telecommunications Shares in Armenia**

The primary source of data was from websites of domestic telecommunication companies and severalweb sites which provide worldwide information on telecommunication 24. I defined market share as the share of total subscribers, summing up fixed-line and mobile subscribers. Then I calculated obtained shares by regions. Table 3 contains mobile phone subscription statistics by company and the number of fixed-line phone subscribers, shareholders %, and market shares by regions.

The results for market share owners of the telecomunication sector in Armenia are as follows Armenia – 0 %, CIS – 38.96%, EU – 5,73%, US – 31,7%, RoW – 23,61%.

## **Supplementary Information on Ownership Shares of Armenian Telecommunications Companies**

**ArmenTel CJSC (brand Beeline):** The Armenia Telephone Company is the largest telecommunications company in Armenia. Its 100% belongs to the second largest Russian mobile network operator VimpelCom (brand Beeline) since 2006. ArmenTel is the second largest taxpayer of Armenia (e.g. in the year 2005 15.3 billion Dram, converted approximately 28 millions euro). Armentel provides 100% of fixed line service, and 19.36% out of mobile services in Armenia. http:///www.beeline.am

**K-Telecom CJSC (trading as VivaCell-MTS)** is a subsidiary of Russian powerhouse Mobile TeleSystems OJSC a leading telecoms operator in Russia and CIS countries, which owns 80% of shares of company, the remaining 20% owned by the Lebanese investment group Fattouch Group.  VivaCell-MTS controlled 73.53 % of the domestic mobile market as at 31 December 2009 <u>http://www.vivacell.am</u>

**Orange Armenia** is 100% daughter of France Telecom (brand name Orange), entered the Armenian market from 5-th November 2009, providing 83% network coverage and access to mobile networks and the Internet in 500 communities (cities and villages) of the republic. By the end of 2009 orange Armenia operated with a share of 7.1% out of total Armenian mobile market. **<u>http://www.orangearmenia.am</u>**

24 http://hitech.mail.am/index.php?category=312&id=2638, http://telecom.arka.am/rus/mobile/2010/01/25/3365.html, http://www.armtown.com/news/ru/pan/20100318/44411/, http://www.internetworldstats.com/asia/am.htmExecutive summary, http://www.totel.com.au/european-telecommunications-research.asp?cid=AM http://www.telegeography.com 79