rafmacalaba/datause-encoder-data huggingface dataset:
adults. It is thought that adults die from a wider range of causes than children, that many of these causes share similar symptoms, and that the reliable retrospective diagnosis of some causes of death in adults may therefore be problematic. Methodological issues in the diagnosis of cause of death in adults by retrospective questioning are discussed in detail in Section 4. The section begins with a review of previous studies of adult and child mortality based on retrospective questioning. The choice between lay interviewers and physicians is then considered, and the consequences of this choice for questionnaire design. The possibility is discussed of developing algorithms by means of which either medical or non-medical interviewers could arrive at a diagnosis on the basis of reported symptoms. Alternative methods of classifying and of ranking causes of death are also reviewed.

Finally, S7ection 5 discusses areas that require further research, and suggests techniques that could be used to validate the survey methods reviewed il! previous sections of the paper. Unless otherwise stated, the term "adult" in this paper refers to persons aged 15-64 years. The reasons fcr this restriction are, firstly, that in most countries this is the age-range in which economic activity and family responsibilities are greatest; and secondly, that the emphasis is on avoidable premature mortality, suggesting that deaths in the oldest age-groups should be excluded. The choice of an upper age limit of 65 years is essentially arbitrary, and it is likely that as death rates at younger adult ages decline, increasing attention will need to be given to deaths at older ages.

The term "cause of death" in this paper refers throughout to medical cause. Investigation of socio-economic, environmental and other risk factors associated with adult mortality is also important, but is outside the scope of this paper.

# 2. REVIEW OF EXISTING INFORMATION ON CAUSE-SPECIFIC ADULT MORTALITY

# 2.1 Introduction

This section reviews existing information concerning cause-specific adult mortality in developing countries. The main sources of information are vital registration and surveys, and the availability and limitations of these sources ars discussed in Section 2.2. Different sources of information use different system43 of classifying cause of death, and Section 2.3 discusses the different approaches, and the consequent difficulties in making comparisons between countries. Finally, Section 2.4 summarises the information on the main causes of death provided by the sources described in Section 2.2, and some general patterns are identified.

# 2.2 Sources of data

In developed countries, data on cause-specific mortality by age are readily available from national vital registration. Although most of the developing countries also have some kind of vital registration system, levels of coverage and reliability are generally low, particularly outside the main urban centres.

3
-32-

Cgua b: The example below illustrates the treatment of goods sold from inventory. To focus on th4s problem and to abstract from depreciation etc., we assume, as in the previous case, that land is the only other asset. On the liability side of the balance sheet the firm has no liabilities so that net worth is the only entry.

TABLE 6 <!-- Start of picture text --> BALANCE SHEET, End of Period 0 INCOME STATEMENT,<br>Period 1<br>ASSETS AO LIAILITIES Lo<br>Revenue (1000) 1200<br>MAO 0 0 MLO Wages (500) 600<br>Cash<br>Materials (100) 120<br>NMA0 NWO<br>Land 2000 2100 Net Worth Inventory (50) 60<br>Inventory 100 Depletion<br>AO 2100 2100 Lo Cash (600) 720<br>Expenses<br>Cash Flow (400) 480<br>Pre-tax<br>ADJUSTED BALANCE SHEET, Period 0<br>The balance sheet<br>ASSETS Ao' LIABILITIES Lo' (A0',Lo') is obtained<br>by revaluing the NMA<br>and NW items by the<br>MAO' 0 0 Lrj3' inflation factor.<br>NMAO I<br>Land 2400 2520 NWo'<br>Inventory 120<br>AO' 2520 2520 Lo'<br><!-- End of picture text --> STEP 1
**The Spatial Distribution of Poverty in Vietnam and the Potential for Targeting**

# **Nicholas Minot and Bob Baulch**

**April 2002**

_Contact information:_ Nicholas Minot is a Research Fellow at the International Food Policy Research Institute (IFPRI), 2033 K Street N.W., Washington, D.C. 20006 U.S.A., email: n.minotgcgiar.org. Bob Baulch is a Fellow at the Institute of Development Studies, University of Sussex and formerly Quantitative Poverty Specialist at the World Bank, Vietnam, email: b.baulch@ids.ac.uk. Senior authorship is not assigned.

_Acknowledgements:_ We thank Phan Xuan Cam and Nguyen Van Minh for their help understanding the Vietnam Census data and Peter Lanjouw for helpful methodological discussions. Paul Glewwe and participants at workshops in Hanoi produced valuable comments on earlier versions this paper. The financial assistance of the DFID Poverty Analysis and Policy support Trust Fund and World Bank Development Economics research Group is acknowledged.
where _FTcokeCSY_ , _j_ , _baseyr_ is the quantity of coke consumed by _j_ in million tons in the base year 2010. _FTliquidfuelCSY_ is the sum of the quantity of gasoline, kerosene, diesel and fuel oil consumed (given in CSY 2012), and _FTotherpetroleumCSY_ is the sum of the quantity of lubricant, bitumen, naphta, etc. consumed (given in the LBL’s China Energy Databook).

is the quantity share of liquids consumed in consumption of total refined petroleum products for industry _j_ . Our model distinguishes between the Gas Mining sector and the Gas Utilities (or Gas Products) sector; most industries purchase only from Gas Products, while a few purchase from Gas Mining for transformation and combustion – Chemicals, Electricity and Gas Products. For all industries _j_ other than Gas Products the consumption coefficient is the _quantity_ of natural gas purchased by industry _j_ divided by the sum of the _values_ of natural gas and gas products purchased by industry _j_ in the base year: In contrast, the energy consumption coefficient for the Gas Products industry is divided by only the value in the Gas Products cell, excluding the Gas Mining cell: We now move on from calculating energy consumption to calculating energy combustion. The above consumption coefficients refer to the purchases of the different fuels. Some of these oil and gas inputs are not combusted but converted to other products such as fertilizer or bitumen. In the Refining sector part of the crude input is combusted but most are converted to liquid fuels or other petroleum products; the un- _ref_ loss_  _ref_ loss_ combusted portion is represented by the “refining loss” coefficient,<sup></sup> _j_ where (1<sup></sup> _j_ ) is the _ref_ loss_ fraction of un-combusted crude input. (For industries other than j=Refining,<sup></sup> _j_ is simply 1, reflecting that 100% of the crude input is combusted.) In the Gas Products (Utilities) industry, gas is purchased from the Natural Gas Mining sector and sold to consumers, that is, there is assumed to be no combustion in this industry. In the Chemicals sector, raw gas is purchased from the Gas Mining sector and part of it is converted to plastics and other products. The _gas_ loss gas_ loss_ combusted portion is represented by  _j_  _Chemical_ . (For industries other than j=Chemical,  _j_  _Chemical_ is simply ‐69‐
<!-- Start of picture text --> 10,000.00 35.0<br>= 30.0%<br>é 8,000.00<br>E 7,000.00 25.0ee 4<br>qj 6,000.00 90.0% &<br>A 5,000.00 a<br>P: 4,000.00 15.0% A<br>2 3,000.00 10.0% &<br>% 2,000.00<br>3 1,000.00 2.0%_—<br>- 0.0%<br>2005 2006 2007 2008 2009 2010 2011 2012 2015 2014 2015 2016 2017<br>Gam System peakdemand mam Total imstalled generation capacity (net) ~=""Heserve margin<br><!-- End of picture text -->
Land is not rated as a major obstacle in large part because the respondents are managers of going concerns. Yet, in the regression analysis,<sup>22</sup> we find that lack of access to land has a negative effect on TFP and the other performance indicators. The entrepreneurs who want to set up firms but cannot because they cannot get access to land are not covered in the survey. Taxes are rated by managers as a major business obstacles, but their responses would be different if the question also identified what public services would have to be cut (or what the level of inflation would rise to) as a consequence of tax cuts. Indeed, in surveys such as PICS and BEEPS, taxes are usually identified by managers as a major obstacle to doing business, whatever the country. Interestingly, infrastructure, which was found to have a strong negative effect on productivity in the regression analysis, ranks last in the results of the factor analysis in Figure 6. A possible explanation is that although infrastructure constraints indeed have a negative impact on performance, we know that the better performing firms complain more about infrastructure. Since the highly productive firms are in the minority in a simple count of companies, their complaints about infrastructure weigh less in comparison to the complaints of lesser performing firms which complain more about lack of financing and taxation. (see Figure 6). In contrast, the regression analysis presented above, shows the importance of infrastructure in enhancing productivity and is more relevant to policy than the ranking of perceptions. We argue therefore that for prioritisation of policy measures, the effects of investment climate constraints on performance or productivity in a regression analysis – and not the ranking shown in this section - should be the guide.

**Figure 6: Combined investment climate measures**

Source: BEEPS II, PICS 2003 Serbia

# **Policy implications for entry, privatization and exit**

As presented above, the results from the international survey show that privately-owned firms, whether privatized or new private, have levels of TFP that are 30-38 percent higher than state-owned firms in the same country. Foreign ownership adds another 23-43 percent to TFP levels. Export orientation adds 22 percent to TFP (for manufacturing 15
|_Hi_<br>_V_<br> (LBP)|5,860,000|2,785,000|
|---|---|---|
|_Bi_<br>_V_ (LBP)|8,535,000|4,548,000|
|0_i_<br>_V_ (LBP)|16,788,000|2,445,000|
|_Bi_<br>_S_<br>(Sq. Meters)|11,859,051|37,501,072|
|_Hi_<br>_S_<br>(Sq. Meters)|5,259,996|21,342,698|
|0_i_<br>_S_ (Sq. Meters)|4,650,000|52,610,000|

**TABLE 2:** Base data on transportation

|Origin,Destination<br>1,1|1,2|2,1|2,2|
|---|---|---|---|
|Mode splits (persontrips)<br> <br>||||
|Car<br>48388|99398|111876|241934|
|Bus<br>1051|2158|2429|5253|
|Minibus<br>6544|13442|15130|32718|
|Taxi<br>4052|8324|9369|20261|
|Distances (one-way)in kilometers||||
|Anymode<br>6.6|11.9|12.3|9.4|
|Traveltimes (one-way)in minutes||||
|Car<br>42.1|53|57|46.4|
|Bus<br>77.6|98.3|104.2|88.8|
|Minibus<br>63.1|81.7|86.8|73.5|
|Taxi<br>58.33|72.33|76.73|65.13|
|1<br>2<br>,<br><br>0.18||0.82||
|1<br>2<br>,<br>_CAP_<br>_CAP_<br>(square meters)<br>3,675,177<br>||17,295,559<br>||
|Car|Bus|Mini-<br>bus|Taxi|
|_m_<br><br>0.5882|0.0893|0.1686|0.8475|
|_m_<br><br>1|2|1.6|1.4|
|_ijm_<br><br>1|1.45|1.25|1.10|
|Wait time(in minutes) 0|6.5|0.5|6|
|Access/egress (in minutes)||||
|Car<br>0|0|0|0|
|Bus<br>10|15|15|15|
|Minibus<br>10|15|15|15|
|Taxi<br>6.03|8.03|8.03|8.03|
|Fare (one-way) in LBP||||
|Car<br>0|0|0|0|
|Bus<br>1000|1000|1000|1000|
|Minibus<br>1000|1000|1000|1000|
|Taxi<br>2000|4000|4000|4000|
|(in Bus)<br>(1,1)|(1,2)|(2,1)|(2,2)|
|0<br>1<br>,<br> <br>17.0865, 0.8|11.0164, 0.8|7.3064, 0.8|7.7022, 0.8|

18
<mark>increase in total VMT. It has also contributed to the decline of the national railway network and made it difficult when determining</mark> _<mark>marginal</mark>_ <mark>extensions to meet growing transport demand to seriously contemplate the introduction or restoration of alternate rail and transit links given the absence of a larger supporting rail networks (as in Japan or Europe)—as opposed to adding more arterial roads. This has reinforced the grip of the road network on meeting transport needs and generating carbon</mark> emissions <mark>35. CO</mark> 2 <mark>emissions associated with the Interstate Highway System are significant, about 7% of total U.S. emissions from fossil-fuel combustion.</mark><sup>30</sup> <mark>These emissions initially increased faster than the growth of total VMT as the System developed. There was a temporary decoupling of emissions growth from</mark> total VMT when higher energy efficiency standards were adopted in the mid-1970s (Figure 4).

<mark>36. However, emissions have resumed growing in tandem with total VMT in the absence of further higher engine energy efficiency standards being adopted or enforced. Until more low-carbon or zero-carbon vehicle engines are deployed, these emissions induced by the physical infrastructure network are to a</mark> large degree locked-in.<sup>31</sup>

**<mark>Figure 4. VMTs and estimated CO</mark> 2** **<mark>emissions from Interstate Highway Traffic 1958-2003</mark>**

<!-- Start of picture text --> VMTs and CO2 emissions from U.S.<br>Interstate Highways 1958-2003<br>450 800000<br>400 700000<br>Highway emissions<br>350<br>VMTs 600000<br>300<br>500000<br>250<br>400000<br>200<br>300000<br>150<br>200000<br>100<br>50 100000<br>0 0<br>195819621966197019741978198219861990199419982002<br>VMTs (billions)<br>CO2 emissions (MtCO2)<br><!-- End of picture text --> _<mark>Source: U.S. Bureau of Transportation Statistics, Lee Schipper (comm.pers.), authors’ calculation</mark>_

> 30 Total emissions from the Interstate Highway System have been estimated by taking one quarter of total emissions associated with road transportation in the U.S. (since the Interstate Highway System represents one fourth of total VMT). This is likely to be an understatement as trucks represent a disproportionate share of travels on Interstates, and emit more per VMT than cars.

> 31 Though Box 3 focuses on CO2 emissions, it should not be interpeted as downplaying the other externalities associated with transportation. In fact, transport projects/networks should be designed to solve transportation problems first. Conversely, solving the carbon externality may do nothing to solve these other externalities. For example, congestion may still be a problem with zero-carbon cars.

15/39
Policy Research Working Paper 5328

# **Abstract**

This paper provides new evidence on the unique role of trade credit and contracting terms as a way for both sellers and buyers to mange business risk. The authors use a novel and unique dataset on almost 30,000 supplier contracts for 56 large buyers and more than 24,000 suppliers in Europe and North America. The sample of buyers and suppliers includes firms of varying size, investment grade, and sectors. The paper finds evidence in support of four important, and not mutually exclusive, reasons for trade credit: 1) as a method of financing; 2) as a means of price discrimination; 3) as a bond assuring buyers of product quality; and 4) as a screening mechanism to gauge buyer default risk. In particular, the analysis finds that the largest and most creditworthy buyers receive contracts with the longest maturities, as measured by net days, from smaller, investment grade suppliers. In comparison, early payment discounts seem to be used as a risk management tool to limit the potential nonpayment risk of trade credit. Early payment discounts are generally offered to smaller, non-investment grade buyers. The results suggest that contract terms are jointly determined by supplier and buyer characteristics.

This paper—a product of the Finance and Private Sector Development Team, Development Research Group—is part of a larger effort in the department to study Capital Structure and Supply Chain Finance. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The author may be contacted at lklapper@worldbank.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
# References

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Arellano, C. 2008. “Default Risk and Income Fluctuations in Emerging Economies.” American Economic Review 98(3): 690–712.

Auerbach, A. J., and Y. Gorodnichenko. 2012. “Measuring the Output Responses to Fiscal Policy.” American Economic Journal: Economic Policy 4(2): 1–27.

Auerbach, A. J., and Y. Gorodnichenko. 2013. “Fiscal Multipliers in Recession and Expansion.” In Fiscal Policy after the Financial Crisis, edited by A. Alesina and F. Giavazzi, 63–98. Chicago: University of Chicago Press.

Auerbach, A. J., and Y. Gorodnichenko. 2017. “Fiscal Stimulus and Fiscal Sustainability.” NBER Working Paper 23789, National Bureau of Economic Research, Cambridge.

Bachmann, R., and E. R. Sims. 2012. “Confidence and the Transmission of Government Spending Shocks.” Journal of Monetary Economics 59(3): 235–249.

Bertola, G., and A. Drazen. 1993. “Trigger Points and Budget Cuts: Explaining the Effects of Fiscal Austerity.” American Economic Review 83(1): 11–26 Bi, H., W. Shen, and S.-C. S. Yang. 2014. “Fiscal Limits, External Debt, and Fiscal Policy in Developing Countries.” IMF Working Paper 14/49, International Monetary Fund, Washington, DC.

<mark>Blanchard, O. J. 1990a. “Comment: C</mark> an Severe Fiscal Contractions Be Expansionary? Tales of Two Small European Countries.” In NBER Macroeconomics Annual 1990, Volume 5, edited by O. J. Blanchard and S. Fischer, <mark>111–116. Cambridge: MIT Press.</mark> Blanchard, O. J. 1990b. “Suggestions for a New Set of Fiscal Indicators.” OECD Economics Department Working Paper 79, Organisation for Economic Co-operation and Development, Paris.

Blanchard, O.J. and D. Leigh. 2013. “Growth Forecast Errors and Fiscal Multipliers.” American Economic Review 103(3): 117-20.

Blanchard, O., and R. Perotti. 2002. “An Empirical Characterization of the Dynamic Effects of Changes in Government Spending and Taxes on Output.” Quarterly Journal of Economics 117(4): 1329–1368.

Bocola, L. 2016. “The Pass-Through of Sovereign Risks.” Journal of Political Economy 124(4): 879–926.

Born, B., F. Juessen, and G. J. Müller. 2013. “Exchange Rate Regimes and Fiscal Multipliers.” Journal of Economic Dynamics and Control 37(2): 446–465.

Candelon, B., and L. Lieb. 2013. “Fiscal Policy in Good and Bad Times.” Journal of Economic Dynamics and Control 37(12): 2679–2694.

Corsetti, G., K. Kuester, A. Meier, and G. J. Müller. 2013. “Sovereign Risk, Fiscal Policy, and Macroeconomic Stability.” Economic Journal 123(566): F99–F132.

Corsetti, G., A. Meier, and G. J. Müller. 2012. “Fiscal Stimulus with Spending Reversals.” Review of Economics and Statistics 94(4): 878–895.

Davig, T., and E. M. Leeper. 2011. “Monetary–Fiscal Policy Interactions and Fiscal Stimulus.” European Economic Review 55(2): 211–227.

15
specialization of the economy. Inadequate macroeconomic framework and poor quality of institutions can also act as magnifiers of the effects of these exogenous external shocks.

**Table 3: Cost of recessions over time and across regions**

|_Dependent variable:_<br>_Average output loss_|[1]||[2]||[3]||
|---|---|---|---|---|---|---|
|Constant|0.014|**|0.009|**|0.013|**|
||(0.004)||(0.003)||(0.002)||
|Dummy LAC|0.001||||||
||(0.004)||||||
|Dummy EAP|‐0.003<br>(0.005)||||||
|Dummy IND|‐0.010|**|||‐0.009|**|
||(0.004)||||(0.003)||
|Sudden Stops|0.010|**|0.012|**|0.010|**|
||(0.003)||(0.003)||(0.003)||
|Banking Crisis|0.004||0.005|*|0.003||
||(0.003)||(0.003)||(0.003)||
|Dummy 80s|||0.004<br>(0.003)||||
|Dummy 90s|||‐0.001||||
||||(0.003)||||
|Dummy LAC*Dummy 80s|||||0.005<br>(0.003)|*|
|Number of episodes|126||126||126||
|Adjusted R squared|0.224||0.140||0.242||
|White test|0.143||0.759||0.317||

_Standard deviation in parenthesis. *, ** the coefficient is significant at 10% and 5% level, respectively._ Appendix I reports the definition and sources of the variables involved in our regression analysis. The external shocks are measured using gross FDI Inflows, dummy variable for sudden stops, gross equity related Inflows, terms of trade, G‐3 (weighted average of Germany, Japan and US) Real Money Market Rate and US Real Money Market Rate. All these variables are measured as average variation over the last four quarters before the turning point take place. Quality of institutions is measured by the political risk index reported by the International Country Risk Guide (ICRG) at the beginning of the recession. Trade openness, financial openness and financial development at previous year are our measures of structural policies. Real exchange rate undervaluation and average inflation 24
of the Indian populace. The Bengal government acceded to the campaign against indigo planters in 1871 and accorded limited rights to tenants in the 1880s. Constant agitation and campaigns in the Indian and British press was one factor that caused British Liberal and Labour governments to concede a very limited degree of local self-government to India after 1883. This nourished the emerging Indian democratic culture and went a little way towards improving primary education and the distribution of relief in the countryside during bad seasons. Constant harassment by the press and emerging Indian public opinion made some inroads into what would otherwise have been the impermeable autocracy of the India Office. In the longer run, it is difficult to imagine the emergence of contemporary India’s vibrant public culture and information economy without some of these initial steps. The issue was not so much the colonial origins of Indian development as of colonized Indians development of social capacity and their maintenance, in adverse conditions, of an entrepreneurial and critical spirit.

# _African comparisons and contrasts_

Historians of sub-Saharan Africa make four key claims about the continent’s pre-colonial inheritance and its early-modern encounter with the West (Vaughan, 2006). This suggests once again that it is the conflict and accommodation between indigenous and colonial institutions, rather than colonial institutions _per se_ , which have determined Africa’s development in the longer term, for better or for worse.

First, the slave trade and the implications of the expansion of slavery for internal African society stunted its growth, even if it did not actively ‘under-develop’ Africa. This is perfectly compatible with AJRs’ data. Indeed, it is built into the idea of an extractive colonial inheritance. But it is important to note that, from the later-nineteenth century, much of West Africa, the key sector of the Atlantic slave trade, was relatively better placed in terms of commercial linkages, GDP per head and institutional stability than east Africa or even Southeast Asia. It remained so throughout much of the twentieth century. This may of course have fostered a type of economy in which powerful magnate ‘gatekeepers’ battened on external connections rather than exploit and develop internal 24