rafmacalaba/datause-encoder-data huggingface dataset:
- Heuveline, Patrick. 2001(a). “Approaches to Measuring Genocide: Excess Mortality During the Khmer Rouge Period.” In D. Chirot and M. Seligman, eds., _Ethnopolitical Warfare. Causes, Consequences and Possible Solutions._ Washington D.C.: American Psychological Association.

- Heuveline, Patrick. 2001(b). “The Demographic Analysis of Mortality Crises: The Case of Cambodia, 1970-1979.” In H. E. Reed and C. B. Keely, eds., _Forced Migration and Mortality_ . Washington, D.C.: National Academy Press.

- Heuveline, Patrick. 2003. “Mortality and Fertility Interactions: New Insights from Recent Population Dynamics in Cambodia.” Processed.

- Heuveline, Patrick, and Poch, Bunnak. 2004. “Individual and Structural Determinants of Marital Stability: Matchmaking, Gender Ratio, and Modernization in Cambodia.” Processed.

- Ichino, Andrea, and Rudolf Winter-Ebmer. 1998. “The Long-Run Educational Cost of World War II: An Example of Local Average Treatment Effect Estimation.” Center for Economic Policy Research Discussion Paper No. 1895. London, United Kingdom.

- Ichino, Andrea, and Rudolf Winter-Ebmer. 2004. “The Long-Run Educational Cost of World War II.” _Journal of Labor Economics_ , 22: 1, 57-86.

- Justino, Patricia. 2001. “Redistribution, Political Conflict and Development in India: The Case of Kerala.” Laboratoire d’Economie d’Orleans Working Paper 2001-5.

- Kiernan, B. and C. Boua. 1982. _Peasants and politics in Kampuchea, 1942-1981._ London: Zed Books, New York: Sharpe.

- Kiernan, Ben 1993. “Introduction.” in B. Kiernan, ed., _Genocide and Democracy in Cambodia: The Khmer Rouge, the United Nations and the International Community_ . Monograph Series 41. New Haven: Southeast Asia Studies, Yale University.

Kiernan, Ben 1996. _The Pol Pot Regime_ . New Haven and London: Yale University Press.

- Kiernan, Ben. 2003. “The Demography of Genocide in Southeast Asia. The Death Tolls in Cambodia, 1975-79, and East Timor, 1975-80.” _Critical Asian Studies_ 35:4, 585-97.

- Kiljunen, Kimmo, ed. 1984. _Kampuchea: The Decade of the Genocide_ . _Report of a Finnish Inquiry Commission_ . London: Zed Books.

- Kingdom of Cambodia and ORC Macro. 2001. _Cambodia Demographic and Health Survey 2000._ National Institute of Statistics, Directorate General for Health and ORC Macro. Phnom Penh, Cambodia, and Calverton, Maryland.

- Meng, Xin, and R.G. Gregory. 2002 (a). “The Impact of Interrupted Education on Subsequent Educational Attainment: A Cost of the Chinese Cultural Revolution.” _Economic Development and Cultural Change_ 50(4): 935-959.

18
# **Annex Table 2: Relevant Policy Variables**

|**Country**|**Yearly Avg**<br>**Inflation**<br>**(%)**|**Contribution**<br>**to PAYG**<br>**scheme**|**Credit**<br>**(% of GDP)**|**Share (%) of**<br>**State-**<br>**controlled**<br>**Insurers**|**Major**<br>**International**<br>**Airline**|**Formal**<br>**Bancassurance**|
|---|---|---|---|---|---|---|
|**Algeria**|3.0|17|12.6|72|||
|**Bahrain**|1.6|12|75.4|0|||
|**Egypt**|7.4|30|43.6|61|Y|Y|
|**Jordan **|3.8|14.5|88.7|0||Y|
|**KSA **|2.0|18|41.8|22|Y||
|**Kuwait**|3.4|17|60.1|0|||
|**Lebanon **|3.0|8.5|73.5|0||Y|
|**Libya**|-0.4|14.25|6.1|44|||
|**Morocco**|1.9|11.89|78.4|0||Y|
|**Oman **|2.9|13|39.4|0||Y|
|**Qatar **|5.5|15|45.9|0|||
|**Syria**|4.8|21|15.7|47|||
|**Tunisia**|3.2|15.5|63.2|20||Y|
|**UAE**|6.7|20|73.8|0|Y||
|**Yemen **|10.5|19|7.2|14|||

Sources: AXCO, 34
MLYTWIAL 9MRY OF THIITY EET SDtIES <!-- Start of picture text --> INwEmur VMIAlES<br>NTH RATE ----------------------------------------------------------------<br>tNIC- CALOAATIN RMSTSAL SECTOA R VIALES<br>PRDUCT ISTICS OF PERID AM AND ECDlC- _EPT IS soCio<br>FINCPh TYPE AM AN AMLYSIS MEIN ITIAL POLICY IETIu FOLITICAL<br>OF STI MC W RllXIES PERIOD WRAILES VAIALES UWIN FACTOS WVtAUIfr<br>FAIlN, eimpet of GP 69 eaitries 1 VOWr Per eqilta Exprfi 9rcmth Popiatein puirth R-s r emt)Ule<br>ANiEZ edlitoy (World (r amtl (polirg ino + + for *ll r qasnl<br>ad -p-dIF Pir) orc ep etIF 1952-70) n. a Vriatihn in (but Africa -) afsu s. btu<br>TAYLO prsented, Silent eilitry Coital 9cuth 40.31. Nobda<br>(1981) or by dmlep- weriables by apandiuta n.s. widthut eqplicit<br>Mnt Leel) coutry pro-tien<br>(fixed f ect) (but n.m. for functien<br>irdLtrial<br>cantries)<br>ExtwmL aid<br>(+ or ns.)<br>MFR ShN that OaWt GDP Smi-ln*ktr. 10 Ets EJIort gradt lint rate i-sr 0.69<br>(193) MGh he a (World cmaitries in Avuag ratrte wighted +<br>favrdbte Iqiact Ra) broed m16-13 by apart Popastien pwmh<br>en pr It g th (31), strIt aef ra<br>be spt MM (19), pract 4<br>matar ha hidhr act. 4<br>a_gmt oil-mqxertiru<br>acktlvlty then countrie<br>ret of ecoy<br>wd beam<br>Latter miju<br>acea mnp<br>_eertd by<br>00rt -11b<br><!-- End of picture text --> <!-- Start of picture text --> FER Test iqucta of DP 30 emi-indstr. 10 yers Vaighted Inveetamt dhre R-sqr t 0.75<br>(1986) - ifaturi.g World contrie. (19-73) grwth of of prct N l of _m<br>ad pWrt Ot Tet With othr mfemcwtur i* type m fEO<br>tmactar duatis" (lets Test en sector Populatfn growth (195):<br>dr#Lcped 1955-3 + rate reallocatfen of<br>caitrim) ad eighted + factors taumd<br>grath of mt efficient<br>apart sectors ard<br>+ extemrality<br>.__.. .. _........................... ....................................................... ... . ...... ........ . ...........................<br><!-- End of picture text -->
<!-- Start of picture text --> Equatorial Guinea: Secondary Sector by GDP Contribution<br>2000 - 2005<br>0,025<br>0,02<br>Secondary sector<br>0,015 Manufacturing<br>0,01 Electricity<br>Construction<br>0,005<br>0<br>2000 2001 2002 2003 2004 2005<br>pencetage of GDP<br><!-- End of picture text --> Sources: Equato-Guinean authorities Bank and Fund staff estimates.

The magnitude of negative macroeconomic consequences of the Dutch Disease will depend on the country economic structure and the stage of development, hence will vary from country to country. A country where a manufacturing sector does barely exist or where the non-oil primary sector is structurally deficient as it has been the case of Equatorial Guinea; there is little to fear about the disease. In that context the oil boom become a blessing, given that oil revenues when properly managed, can play a special and critical role in overall economic development and poverty reduction in low-income countries like Equatorial Guinea. Although the GDP per capita has substantially increased from US$ 346 in 1995 to US$ 3391 in 2004 due to the coming on stream of important oil fields, EQG remains among the poorest country by the standard of living conditions and the level of development.

<!-- Start of picture text --> Equatorial Guinea: GDP by Sector Contribution 2000 - 2005<br>100<br>80<br>Primary sector<br>60<br>Primary sector-Oil<br>40 Secondary sector<br>Tertiary sector<br>20<br>0<br>2000 2001 2002 2003 2004 2005<br>Percentage of GDP<br><!-- End of picture text --> Sources: Equato-Guinean authorities Bank and Fund staff estimates.

Considering the pattern of EQG traditional agriculture and export sectors before the coming on stream of important oil fields in 1996, the sector was already in decline despite the government’s efforts. Cocoa, coffee and timber have traditionally been the country’s main source of income. During the early period of independence, Equatorial Guinea experienced a drastic economic decline; GDP per capita in current terms fell 17
for local governments. Chile, Colombia, Mexico, and Peru issued a $1.3 billion cat bond, through the World Bank, to protect against major earthquakes.<sup>9</sup> Insurance markets can generate the benefits one can expect from them only if there is a good cooperation between governments, regulators, and private actors. Countries with high penetration of disaster insurance typically have a public system to cope with the largest risks and mandatory disaster risk insurance. With climate change creating more uncertainty and thus making the pricing of insurance more difficult, the need for public‐private partnership will become even bigger. It is also important that many climate change impacts are likely to remain uninsurable, either because they are too big or because they are too frequent.

# _What needs to be done?_

In addition to adopting similar measures for banks<sup>10</sup> (e.g., governance, risk surveillance, supervisory approaches), key challenges to adapt to climate change for the insurance sector include:

- **_Data, knowledge, and modeling limitations._** Limited data on hazard, vulnerability, and exposure impedes the development of cost‐effective insurance‐based solutions. The complex nature of disasters creates volatility and uncertainty surrounding loss estimation which can only be addressed through improved data. Further, when risk‐based premiums are applied, this paucity of data can impact the affordability of cover due to the positive correlation between risk and pricing. For infrastructure investments, insurers require data to assess the climate resilience and carbon‐intensity throughout its lifecycle (Geneva Association, 2018).

- **_Disaster planning and budgeting._** In many countries, the policy and regulatory environments, institutional capacities and mandate, lack of coordination, and sometimes willingness to share data within and across government, hamper the implementation of disaster risk finance and insurance measures. Additional support is required to build understanding and improve quantification of socio‐economic risk.

- **_Market failures and innovation needs._** The limited uptake of catastrophe risk insurance by households or firms is the result of a market failure that reduces the incentive to adapt, including to purchase insurance.  It can be attributed to a combination of limited demand to purchase such products, and at times weaknesses within the domestic insurance market to supply these products – demonstrating that capacity building is needed on both the demand and supply side. Innovation within the insurance sector can help address existing domestic market failures with support from governments and the international re/insurance markets.

> 9 The World Bank is supporting Jamaica to issue a cat‐bond to insure against hurricanes, floods and earthquake. 10 Also see IAIS (2018).

22
<!-- Start of picture text --> Figure 8a: Linkages<br>Effect of social protection on shocks impacts between social protection,<br>300000 timing and consumption<br>sensitivity to shocks,<br>200000 N=3,059<br>100000<br>Notes: Question: Does<br>0 month of year that<br>household receives social<br>‐100000<br>protection matters? The<br>‐200000 main outcome of interest:<br>households’ consumption<br>‐300000<br>expenditure, defined as<br>‐400000 either per capita food or per<br>capita household<br>‐500000 consumption. Confidence<br>SP as shock absorber<br>intervals are indicated by<br>the blue vertical lines.<br>Figure 8b: : Linkages<br>Effect of social protection on shocks impacts between social protection,<br>150000 timing and consumption<br>sensitivity to shocks,<br>N=3,059<br>100000<br>69000<br>Notes: Question: Does<br>50000 50000 49000<br>quarter of year that household<br>20000 receives social protection<br>0 matters? The main outcome<br>Shocks Shock x SP x Shock x SP x Shock x SP x Shock x SP x of interest: households’<br>‐50000 ‐49000 Quarter 1 Quarter 2 Quarter 3 Quarter 4 consumption expenditure,<br>defined as either per capita<br>‐100000 food or per capita household<br>consumption. Confidence<br>‐150000 intervals are indicated by<br>SP as shock absorber<br>the blue vertical lines.<br>Poiint estimate, CFA<br>Point estimate, CFA<br><!-- End of picture text -->

# **Incentive Effects**

One long-standing incentive-hypothesis is that providing social assistance to households can create incentives and cause the households to become more sensitive to external shocks for behavioral reasons, e.g., through Moral Hazard Effects.<sup>17</sup> This can limit the soundness of providing social assistance because the ability of households to cope with shocks such as droughts and diseases is limited in such cases. In general, social transfers can either aid or potentially disincentive households coping to realized shocks. The “benefits” of social transfers

> 17 Or more broadly Asymmetric Information.

30
14

- 4 Anklial Stock Demand INV(a) - INV(RPG(c),P(I),PP(I), LEINV(a)])

- 5 Livestock comm. prod QP(I) - QPIPP(l),RPG(c),P(I), INV(a),L[QP(l)J,TIma)

- S Per cap cons, Ivstk PCL(I) - PCL(RP(I),POF,PCON, commodity I LCPCL(I)])

- 1 Not exports, lvstk NEXP(I) - NEXP(PPUS(l),INVUS(I)) commodity I

- 2 Consumer Price,Ivstk RP(I) * RP(PP(l), L[RP(l)]) commodity I

- S Market clearlng, QP(I) - NEXP(l) - POP*PCL(I) 'vstk commodity I

- I Consumer price of PTORT - PTORT[RPG("malze")] tortillas

The thirty-three equations ilsted solve for thirty-three endogenous variables. Consumer prices are determined as a function of producer prices for only two livestock commodities, beef and pork. For all other livestock commodities, a thue serles of producer prices was not available. In these cases, the market clearing equation solves for a consumer price. The notatlon Lt.] Indicates a lagged value of the variable shown Inside the brackets.

Model parameters wore estUmated using multivarlate linear regresslon methods (OLS and 2SLS) using data from the Mexican MinIstry of Agriculture and Water Resources (on crop and livestock production, prices. stocks, hnports e.id exports), the Mexican Central Bank (price Indices), Ministry of Programming and Budgetig
# **Figure 10**

## **Variance decomposition over time, by degree of financial depth**

## **(a) Gross inflows**

## **(ii) High financial depth**

<!-- Start of picture text -->
1.0 1.0<br>0.9 0.9<br>0.8 0.8<br>0.7 0.7<br>0.6 0.6<br>0.5 0.5<br>0.4 0.4<br>0.3 0.3<br>0.2 0.2<br>0.1 0.1<br>0.0 0.0<br>1998 2000 2002 2004 2006 2008 2010 2012 2014<br>Global share Group share Country share<br><!-- End of picture text -->

<!-- Start of picture text -->
1.0 1.0<br>0.9 0.9<br>0.8 0.8<br>0.7 0.7<br>0.6 0.6<br>0.5 0.5<br>0.4 0.4<br>0.3 0.3<br>0.2 0.2<br>0.1 0.1<br>0.0 0.0<br>1998 2000 2002 2004 2006 2008 2010 2012 2014<br>Global share Group share Country share<br><!-- End of picture text -->

## **(b) Gross outflows**

## **(ii) High financial depth**

<!-- Start of picture text -->
1.0 1.0<br>0.9 0.9<br>0.8 0.8<br>0.7 0.7<br>0.6 0.6<br>0.5 0.5<br>0.4 0.4<br>0.3 0.3<br>0.2 0.2<br>0.1 0.1<br>0.0 0.0<br>1998 2000 2002 2004 2006 2008 2010 2012 2014<br>Global share Group share Country share<br><!-- End of picture text -->

<!-- Start of picture text -->
1.0 1.0<br>0.9 0.9<br>0.8 0.8<br>0.7 0.7<br>0.6 0.6<br>0.5 0.5<br>0.4 0.4<br>0.3 0.3<br>0.2 0.2<br>0.1 0.1<br>0.0 0.0<br>1998 2000 2002 2004 2006 2008 2010 2012 2014<br>Global share Group share Country share<br><!-- End of picture text -->

56
heterogeneity of the informal sector and the need to analyze each of its segments independently, their studies do not discuss the home-based work sector (Cunningham and Maloney 2001, Maloney _forthcoming_ ). This is partly due to the absence of large sample data sets that would allow for economic analysis (Chen 1999). Instead, the majority of the evidence is derived from qualitative case studies and small-sample interview data by sociologists and anthropologists (Chen 1999, Prugl 1997, Carr 2000). The stylized picture that is forwarded by these researchers is that home-based work is primarily undertaken by women and their children (Chen 1999) who perform simple, repetitive, labor-intensive tasks (Arriagada 1998) for long hours and low wages. Since the work is performed in the privacy of one’s home, the workers are subject to poor work conditions, few legal protections,<sup>3</sup> and isolation (WIEGO 2000).

While some of these conclusions are indisputable, others would benefits from further analysis with a large, random sample. Jointly analyzing the data on home-based workers and data from a control group of workers with similar characteristics who are not home-based workers will reveal whether home-based workers are paid less and work more because of their particular labor market constraints, or if they are doing just as well as non-home-based workers with similar constraints.

This paper uses household surveys in Brazil, Ecuador, and Mexico to empirically test the findings from the small sample ethnographic research. The paper addresses three primary questions: who are home-based workers?, are they underpaid?, and do they work extraordinarily long hours? The analysis selects comparison groups of non-home based workers and attempts to control for characteristics that may be responsible for the

> O’Connell (1999) give a full exposition on this concept.

> 3 Many countries have legislation governing market work that is performed in the home (ILO 1995), but

3
26 advantage indexes for a number of supplier industries. Other likely beneficiaries are service industries such as consulting. A recent OECD study pointed out that various forms of technology licensing are likely to form a large part of the trade in environmentally sensitive goods (OECD, 1992). For example, a Japanese enterprise having developed an environment-friendly process sells the license abroad. Data for the non-merchandise trade flows are difficult to obtain, however.

To get some insight into supplier industries, RCA indexes were calculated for industries that can be assumed to supply machinery to environmentally-sensitive production. Table 13 identifies nine categories of machinery used in industries like pulp and paper (2), metals (3), power generation, heating and cooling equipment, cleaning machinery, and non-electrical 3 machinery".

From Table 13, industrial countries clearly have revealed comparative advantage in a larger number of industries which are suppliers of machinery to environmentally-sensitive industries. Among the developed countries, Austria and Japan have improved their comparative advantage in five and three categories, respectively. Although Japan is exporting less of environmentally sensitive goods, it has gained markets in machines that are used by these industries. This would indicate that investments to reduce social costs of environmental damage would have brought private benefits in terms of development of new technologies and industries. Germany has maintained its traditionally strong position with some minor market declines. Finland maintained its competitiveness during the period in five industries, and RCAs increased in five categories. Norway shows as being non-competitive in machinery exports. Sweden was an important supplier of all categories covered, but has lost its comparative advantage in two industries. The US has lost comparative advantage ia some industries. As with exports of environmentally sensitive goods, developing countries are slowly increasing their shares in all categories of machinery exports.

I **hein are** SrrC (Rev2) 725, 726, 728, 736, 737, 741, 745, 749, 773.
- 12 -

product (PCP), neighbor's military expenditure (NMES), and own military expenditure (MES) were dropped and regresslome run without these observations. When the two top and bottom outliers by ORT or MES are dropped, the same non-linear relationship as the full sample holds, with only small re'uctions in significance levels for some of the regressions. Dropping the 10 highest and lowest outliers by per capita product and NMES also does not change the non-linear relationship. However, if we drop the 10 outliers by growth rates and MES, the hypothesized relationship does not always hold. In sum, dropping individual outlier observations leads to a conclusion similar to dropping regions. A few extreme outlier observations are not producing the fill sample results, but a subset of observations are.

Additional, but less rigorous, information is provided by Fiare 2 on the next page. It plots the mean MES against the annual growth rate of per capita GNP from 1970-89 for seven regions: South Europe, North Africa, Middle East (minus Saudi Arabia), South Asia, East Asia, Sub-Saharan Africa and Latin America. The plot shows a wn-linear relationship with the growth rate by regions increasing with MES up to roughly 4 percent and then decreasing.

In =m, for the full sample of 71 countries, we find a significant, non-linear relationship between milex and the growth rate. The coefficients of MES and MES2 indicate that the change from a positive to a negative relationship between growth and MES comes when MES is between 4% and 9% of GNP, depending on which regression one uses. However, this result **is** being driven by the 24 countries in the sample from Asia, the Middle East, North Africa and Southern Europe, and the specific coefficients are not robust. When the regressions are run without these countries, there is no significant relationship - - positive or negative between MES and the growth rate. The only general conclusion from the growth regressions is that there is no evidence of a negative relationship between milex and growth in peacetime until the military expenditure share is quite high.
# **3. METHODOLOGICAL FRAMEWORK**

3.1 The development of a social policy framework capable of meeting the objectives defined above, requires an empirical evaluation of the efficacy of current social policies as well as the potential for improvement from the adoption of alternative instruments. This analysis will be based on the household survey undertaken specifically for the current study, and will make use of analytical techniques developed in the income distribution literature.

## **HOUSEHOLD SURVEY**

3.2 The empirical analysis will be based on the household survey conducted by OPSM in May 2002. The survey covered 2,505 households distributed across 39 localities nationwide with more than 5,000 inhabitants. The survey is representative of urban areas at the national level, and also at the level of the Metropolitan Area and the five major regions of the interior. The sampling errors is plus or minus 1.96% at the national level, and plus or minus 5.0% at the regional level.

3.3 The most innovative aspect of the survey is the combination of expenditure data and consumption patterns for infrastructure services with data on household income and socioeconomic characteristics. In this sense, the survey can be contrasted both with utility customer databasesthat offer extensive information on consumption patterns without any accompanying information on socioeconomic characteristics of the householdand national household surveysthat offer extensive information on socioeconomic characteristics without reporting expenditure patterns on utility services<sup>5</sup> .

3.4 In November 2002, OPSM conducted a second household survey with wider poverty measurement objectives, which collected detailed information on the structure of household expenditure, including expenditure on infrastructure services.

## **MEASURING CONSUMPTION**

3.5 The following procedure was adopted to estimate consumption of infrastructure services from the survey data. First, for the 25%-35% of households that were able to provide the interviewer with a copy of their utility bill, the level of consumption could be

> 5 The family expenditure survey of 1997 was the last to provide official data for expenditure on utility services. However, even this survey does not present a very detailed breakdown given that expenditures are presented in four broad categories, namely: water and electricity combined, telephone and post combined, fuels and transportation.

13
<!-- Start of picture text --> 0.70<br>0.65<br>a]<br>3<br>ae]<br>a<br>3 0.60<br>v<br>4fs}<br>ca<br>w<br>0.55oO<br>0.50<br>1950 Cohort (which1960 decade individuals1970 are born in) 1980<br>=—e— Developing economies == High-income economies<br><!-- End of picture text -->