rafmacalaba/datause-encoder-data huggingface dataset:
five percent of the women born between 1950 and 1964 had never been married. One way the marriage market seems to have adapted to the unbalance is for the age difference between partners to have been reduced. Figure 11 displays by birth cohort the average age difference between partners.<sup>13</sup> The birth cohorts aged 10-14 and 15-19 in 1979, the ones that would get married in the subsequent decade, have a lower age difference with their partner than the preceding and subsequent birth cohorts. Table 4 also reports that the same birth cohorts of women are more likely to have a partners younger than them. The evolution of the schooling differences between partners across birth cohorts, reported in table 4 and plotted in figure 11, is also interesting: the average schooling difference between partners is at its lowest point for women aged 5 to 14 in 1979. As shown in sections II and V, the Khmer Rouge period had also lasting impacts on average educational attainment of individuals: educated individuals, especially males, were more likely to die and individuals, again males in particular, who were of schooling age during the period have much lower schooling levels than preceding and subsequent birth cohorts. In the years following 1979, there was therefore a shortage of educated men eligible for marriage and the average schooling attainment of men and women became closer.

After the mortality crisis of the 1970s, two potentially opposite forces were thus characterizing the Cambodian marriage market: on the one hand, eligible males were in short supply, reducing the choice opportunities for females, but, on the other hand their age and schooling differences with their husbands decreased, which should have increased their bargaining position inside the household. Heuveline and Poch (2004) study the impact of the low male to female ratio on marital outcomes, but do not find evidence that it increased marital instability.

# **IV.  Health and Disability**

Since the Khmer Rouge period was a period of war, starvation and violence, it is reasonable to expect that it had an impact not only on mortality but also on morbidity. This section attempts to measure the long term consequences of the period on the health status of Cambodians in 2000.

Poor nutrition during childhood and adolescence are likely to result in stunting. The Cambodian Demographic and Health Survey contains anthropometric measurements of women aged 15 to 49. Figure 12 plots the average height and the proportion of women above 150 cm (women under 150 cm are considered to be at nutritional risk). Women

> 13 The variable is the age of the current partner, not necessarily the first husband. The data is reported in table 4.

10
-. 1992. Estonia: Draft country Economic Memorandum, Volume III Statistical Tables. (September).

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> -. 1993h. Belarus, Country Economic Memorandum: First Steps in Transition...ECIV, 1993 No. 11349-BY.

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141
- 35 -

in the region to jointly design and market standardized high quality tonnage, like the E3 tanker, demonstrate their deterninedness to stay in the market. The E3 designation stands for European, Environmental, and Economical.

Table 9: THE WORLD'S LEADING SHIPBUILDING NATIONS Ranking by Percentage Share in Tonnage on Order

|**198**|**5**|**198**|**8**|**199**|**2**|
|---|---|---|---|---|---|
|Japan|43.3 %|South Korea|32.5 %|Japan|39.0 %|
|South Korea|19.0 %|Japan|25.8 %|South Korea|21.1 %|
|Brazil|6.1 %|Yugoslavia|5.0 %|Denmnark|5.4 %|
|Taiwan|3.5 %|Taiwan|3.9 %|China|5.1 %|
|China|3.4 %|Brazil|3.5 %|Taiwan|3.3 %|
|Germany|2.1 %|China|3.3 %|Romania|2.9 %|
|Poland|2.0 %|Germany|3.2 %|Brazil|2.7 %|
|Spain|2.0 %|Poland|3.1 %|Germany|2.4 %|
|Yugoslavia|1.9 %|Spain|3.1 %|Great Britain|2.4 %|
|Romania|1.7 %|Italy|2.7 %|Poland|2.3 %|
|Denmark|1.7 %|Romania|2.3 %|Spain|2.3 %|
|U.S.A.|1.5 %|Denmark|1.9 %|Croatia|2.1 %|
|All Others|11.8 %|All Others|9.7 %|All Others|9.0 %|
|||**Total Tonnage**<br>year-end re|on**Order**<br>cords|||
|1985: 46,696,|555 dwt|1988: 38,536|,093 dwt|1992: 68,672,|316 dwt|

Source: Fairplay. _'Newbuilding Supplement"._ Various issues. London 69. The shipbuilding industries in the FSU and East European countries, and their ability to attract orders will remain an important factor in the shipbuilding market during the remainder of the 1990s. Yet, while Poland and Romania have been able to establish their yards firmly in the international market, there is much doubt regarding yards in the different maritime republics of the FSU. Many of these yards have b_en and remain engaged in the construction of naval vessels. The extent and pace in which such naval yards can be converted to build merchant tonnage will determine these countries' prospects to partake in international shipbuilding activities. The most cumbersome obstacle to overcome are centralized and archaic management organizations which stand in the way towards required reforms.
19

**Figure 10: Estimated Demand and Supply**

<!-- Start of picture text --> 8.2<br>Supply equation<br>8.15 Demand equation<br>8.1<br>8.05<br>8<br>7.95<br>7.9<br>7.85<br>7.8<br>7.75<br>2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011<br><!-- End of picture text --> Source: Constructed by the authors.

**Figure 11: Adjustment on quantities**

<!-- Start of picture text --> 8<br>7.95<br>7.9<br>7.85<br>7.8<br>Historical quantities<br>Fitted quantities<br>7.75<br>7.7<br>2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011<br><!-- End of picture text --> Source: Constructed by the authors.
# **1.  Introduction**

Empirically estimating government spending multipliers requires the isolation of a source of variation in government spending that is likely to be uncorrelated with contemporaneous macroeconomic shocks. In this paper, I construct an instrument for fluctuations in government spending, drawing on a near-comprehensive dataset of about 60,000 individual loans from official creditors (primarily multilateral development banks and bilateral aid agencies) to developing country governments over the period 1970-2010, as recorded in the Debtor Reporting System database of the World Bank. My identification strategy exploits two key features of this data. First, for many developing country governments, loans from official creditors are a major source of financing for public spending. In my largest sample of 102 countries, disbursements on these loans account for 11 percent of government spending for the median country-year observation, while the 75th and 90th percentiles correspond to 19 and 28 percent, respectively. Second, rather than simply financing the difference between government expenditure and revenue in a given period, these loans typically are tied to specific multi-year spending projects, and accordingly disburse over a period of several years following the original commitment of the loan, with disbursements linked to stages of project implementation.

My core identifying assumption is that the decision to approve a loan in a given year, and to embark on the associated spending plans, is uncorrelated with shocks to growth occurring in subsequent years when the spending plans are implemented and the actual loan disbursements take place. If this identifying assumption holds, and if loan disbursements follow a schedule specified at the time of loan approval, then disbursements occurring in the years following loan approval will also be uncorrelated with contemporaneous macroeconomic shocks. Moreover, the long disbursement profiles observed on these loans imply that disbursements occurring in the years following loan approval are substantial: for the average loan in my dataset, only 22 percent of the original commitment is disbursed in the year that the loan is initially approved, and only a further 18 and 13 percent are disbursed in the first and second years following the approval year. The remaining nearly 50 percent of the loan is disbursed three or more years after loan approval. This in turn implies that the bulk of disbursements on loans from official creditors in a given country-year reflects loan approval decisions made in many previous years, and -- crucially -- before contemporaneous macroeconomic shocks are known.

An immediate concern with strategy is that, even though loan approvals are by definition made prior to the realization of macroeconomic shocks that occur during the subsequent disbursement 2
(Appendix Tables 3 and 4). Across the board, hospitals are more likely to have all pieces of equipment, regardless of urban/rural location.

**Figure 8** : Availability of at least one functioning thermometer, stethoscope, and sphygmomanometer by facility type (hospital vs. non-hospital) and urban/rural location* in Kenya, Sierra Leone, and Tanzania

<!-- Start of picture text --> Hospitals Non-hospitals<br>Kenya - Rural<br>Kenya - Urban<br>Sierra Leone - Rural<br>Sierra Leone - Urban<br>Tanzania - Rural<br>Tanzania - Urban<br>0 10 20 30 40 50 60 70 80 90 100<br>Facilities with all basic equipment (%)<br><!-- End of picture text -->

> * Sierra Leone sample did not have any rural hospitals, and therefore a single dot for the “Sierra Leone – Rural”

Beyond the availability of basic equipment, the SDI also reports on facility capacity for sterilization, which is needed to ensure that equipment and reusable supplies are clean and do not spread infection. For instance, in Kenya, sterilization<sup>18</sup> is available in 87% of hospitals but only 72% of non-hospitals.

# Availability of treatment supplies

Indicators from the SDI survey can paint a broad picture of availability of treatment supplies for a pandemic. While there are no widely recommended treatments for COVID-19, some emerging evidence suggests that chloroquine and azithromycin may promote virologic cure.<sup>19</sup> The three SDI surveys analyzed for this exercise did not collect information on chloroquine availability at facilities (though older surveys did, when chloroquine was more commonly used for malaria treatment) but did collect information on availability of azithromycin. The SDI surveys are unique in that they require that enumerators verify the availability of unexpired medicines by visual inspection rather than asking facility employees to simply report availability. Azithromycin was available and unexpired in 37% of facilities in Tanzania, 32% in Kenya,

> 18 Sterilization is defined as the presence of an autoclave, an electric boiler/steamer, an electric dry heat sterilizer or a non-electric dry heat steamer/sterilizer.

> 19 Gautret, Philippe, et al. "Hydroxychloroquine and azithromycin as a treatment of COVID-19: results of an openlabel non-randomized clinical trial." _International Journal of Antimicrobial Agents_ (2020): 105949.

17
- Fujita, Masahisa, Paul Krugman, and Anthony Venables, The Spatial Economy: Cities, Regions and International Trade (Cambridge, MA: MIT Press, 1999).

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Ghani, Ejaz (ed.) The Services Revolution (Oxford, UK: Oxford University Press, 2010b).

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Helsley, Robert, and William Strange, “Coagglomeration”, Working Paper (2010).

Helsley, Robert, and William Strange, “Matching and Agglomeration Economics in a System of Cities”, Regional Science and Urban Economics 20 (1990), 189-212.

Henderson, J. Vernon, “Marshall's Scale Economies”, Journal of Urban Economics 53 (2003), 1-28.

24
# 2 De...ning Firm-Level Domestic Value Added

We use two micro data sets in this paper: Chinese customs transaction-level trade data from 2000 to 2007, and the Annual Surveys of Industrial Firms from the National Bureau of Statistics of China over the same period. Readers are referred to the appendix for details.<sup>12</sup> For the ease of exposition, we ...rst focus on processing exporters, which are required by law to sell all their outputs abroad and may import materials free of duties.<sup>13</sup> In Section 3.3, we will extend our methodology to study non-processing exports and thus aggregate exports of China.

Let us ...rst de...ne the main variable of interest –domestic value added in exports (DVA), starting from the accounting identity of a ...rm’s total revenue. A ...rm’s (i) total revenue (PYi), by de...nition, consists of the following components: pro...ts, (�i) ; wages (wLi) ; cost of capital (rKi) ; cost of domestic materials �P<sup>D</sup> Mi<sup>D</sup> �, and cost of imported materials �P<sup>I</sup> Mi<sup>I</sup> �: Some domestic materials may embody foreign content, while some imported materials may embody domestic content. Let us denote the foreign content in domestic materials and domestic content in imported materials by �<sup>F</sup> i<sup>and�D</sup> i<sup>,respectively.ThenP DM D</sup> i can be

> 12We employ the procedures commonly used to organize these data. We remove trade intermediaries, identi...ed by the methods proposed by Ahn et al. (2011), in the customs data. We also remove import and export transactions with China itself. As pointed out by Liu (2013), China’s re-imports from itself accounted for about 9% of its total imports. These abnormal trade ‡ows could arise from tax and transport cost saving incentives.

> 13China’s Customs regulates processing trade under several regimes, with pure assembly (PA) and import and assembly (IA) being the two main types. The main di¤erence between these two regimes lies in the allocation of control rights of the imported inputs. In the PA regime, a foreign ...rm supplies components to a Chinese assembly plant and retains ownership and control over the imported inputs throughout the production process. In the IA regime, a Chinese assembly plant imports components of its own accord and retains control over their use. Readers are referred to Feenstra and Hanson (2005) for a more detailed description of the two regimes. While this distinction will not a¤ect our DVAR estimates, it may a¤ect the way one should model ...rm sourcing decisions. See Fernandes and Tang (2012) which exploits these regulatory di¤erences to study the organizational form of o¤shoring. Later on we will report regression results separately for the two types of processing.

6
Saharan Africa, lag because of a combination of low starting points and difficult circumstances (Easterly 2009; Clemens and others 2007; World Bank 2010).

**Figure 1. Current global distance to the MDGs is wide ranging**

<!-- Start of picture text --> a. Distance of latest indicators to the 2015 goals<br>100%<br>96%<br>90% 91%<br>90%<br>88%<br>80%<br>80%<br>77%<br>70%<br>69%<br>67%<br>60%<br>57%<br>50%<br>40%<br>30%<br>20%<br>10%<br>0%<br>b. Distance to the trajectory to be on track to achieve the goals by 2015<br>100%<br>100% 100% 100% 100%<br>90% 90% 91%<br>80%<br>79% 79%<br>70%<br>68%<br>60%<br>50%<br>40%<br>30%<br>20%<br>10%<br>0%<br>MDG 1.a MDG 1.c MDG 2.a MDG 3.a MDG 3.a MDG 4.a child MDG 5.a MDG 7.c MDG 7.c<br>extreme hunger primary gender parity gender parity mortality maternal access to safe access to<br>poverty completion (primary) (secondary) under five mortality water sanitation<br>rate<br><!-- End of picture text --> _Source:_ Authors’ calculations based on data from the World Development Indicators database. _Note:_ Distance to goal achieved in this graph is a weighted average of the latest indicators, using population weights in 2009.

Behind those aggregate numbers, however, there is a great diversity of performance across indicators, countries, and groups of countries that requires further analysis. Bourguignon et al. (2010), Leo and Barmeier (2010), and ODI (2010) showed that progress has been more heterogeneous than is shown by the aggregate figures. Although the MDGs were conceived as global targets to spur development efforts and support to poor countries, it is necessary to measure and describe progress at the country or other level to better understand advances and remaining gaps.<sup>5</sup> Global and regional summaries typically amass data for countries of dissimilar

> 5 Fukuda-Parr and Greenstein (2010) sustain that development goals are not ―hard planning targets‖ but rather guidelines ―meant to encourage countries to strive for accelerated progress‖. Their approach consists in comparing rates of change in development indicators before and after 2001, the year the United Nations outlined its strategy for achieving the MDGs, assuming that progress should be measured against the moment MDGs were adopted.

3
54 Economic Parameters of Derorestation

# Appendix to Section 5: The Mathematica Program

## **Dame Case**

CCGraphics 'GraphicsOff[General::spellI Off[General::spellJ Off[Power::infyj Off[Solve:: ifutl]

### (* program control variables '1

dmax-30u ( highest land index, d '1 dateps-20: I initial number of land index increments J tsteps-207 P number a! time periods * thresh **5:** P tolerated excessdemand '3 adj - 0.006: C' initial price adjustment factor J) iterations - aO r' set iteration counter ') propertyrights **-** True; nonpositive[x_l Mlntx,01; **(4** economic variables *) iagx-60 p initial agricultural output quantity **)** agx-60; C' initial agricultural output quantity **)** aqel-li C' price elasticity for agricultural demand **1)** arevenue-agx*(iagx/agx)(1/agqel): p gross revenue from land in agriculture ' a-5i C' cost coefficient for agriculture '3 pproduction-10; C' plantation productivity factor **)** b-l; C' plantation cost factor *' fstock-28: P timber stock in old growth forest '2 ioyofactor - 501 1' productivity factor of effort in logging 21 c-1.3: ' logging cost factor ) r-.5; (' discount rate **)** w - 200; C' wage rate ') price - Table[30,(i,tsteps)1; (r timber price 'I lltax - 1; P logging tax per land unit '3 lttax - 0; Ir logging tax per timber unit 2 pltax **-** O; **(0** plantation tax per land unit ' pttax - **=; (0** plantation tax per timber unit 'I demand :- Table[lO000(pricel(ill)'A(l-1,fi,tsteps-l1I; p timber demand ) acost[d_ D- a*du C' per-land transport cost of agriculture '3 pcost[d_I :- b'd; p d per-timber transport cost of plantation "3 fcost[d ] : c'd; - per-timber transport cost of logging I plantprod[e_] := Sqrt[e] pproduction; -4 plantation output as function of effort *1 plantprof(d_,p_,eg_I := E^C-r)C(Cp - pcost(d] - pttax) ***** plantprod[e] - ***** a a - pltax); (* plantation profit J templ **-** D[plantproffd,p,el,e]; C' first derivative of profits ') temp3 - Simplify[e /.First[Solve[templ==O,e]]J; (I profit masximizing effort in plantations ) temp2 - D[templ,ej /. e->temp3; I' second derivative of profits 'I logprod[e ,q_j :- Cl-EC-qle)) fstock; -( logging output as function of effort I) logprof(d_,p e_, :- ((p - pcost[d] - lttax) ' logprod[e,Logefactorl - w ' a -Tiltax); C' logging profit I temp4 = D(logprof[d,p,e],eJ; C' first derivative of profits I temp6 = Simplify[e /.First[Solve[temp4=O,e]J1;
information needs to be gathered much more extensively and with more attention to survey design. The WHO could play an important role by formulating and making available appropriate standard survey instruments that local universities or public health research institutions could use to quickly begin surveys when a disease outbreak occurs in some part of the world. Such information could also help governments design more effective information strategies to address the most significant information gaps or confusions among the public. More research is also needed on the feedback from private behavioral changes to disease epidemiology. Just how effective were private behavior changes in helping curb SARS, for example? Could the same effects have been achieved by less extreme or different behavior changes? Information of this kind could help public health authorities design better disease surveillance and control strategies which draw upon the active participation of the public while reducing inadvertent or excessive sideeffects on the economy. Collaboration across diverse scientific disciplines such as disease biology, epidemiology, economics and psychology is likely to prove especially fruitful in this research program.

23