rafmacalaba/data-use-mentions-extended huggingface dataset:
||**wetland by 0.16 m**<br>**of SLR**||**wetland by 0.16 m**<br>**of SLR**|
|---|---|---|---|
|West Africa|-0.07|Australasia|-0.12|
|Central Africa|-0.13|North Africa|-0.21|
|East Africa|-0.12|West Asia|-0.22|
|South Africa|-0.17|West Europe|-0.17|
|South Asia|-0.1|Central Europe|-0.2|
|South-East Asia|-0.12|East Europe|-0.19|
|East Asia|-0.22|Canada|-0.06|
|Central Asia|0|USA|-0.24|
|Meso-America|-0.18|South America|-0.19|
|Brazil|-0.09|-|-|

Each of the 140 GTAP9 database regions has been associated to one macro-region of Table 1. The percentage loss in coastal wetland (Table 1) has been multiplied by the percentage of erodible coast and applied to the whole coast. For the European regions, the shares of erodible coast have been obtained from the Eurosion project (www.eurosion.org), while for the remaining countries we have adopted the 70% value suggested by Bird (1987, 2010). Considering which fraction of total coast is suitable for agricultural and other productive activities we have estimated the fraction of agricultural land which is lost when SLR equals 0.16 meters. Scaling up, we got the share of productive land which is lost for one meter of SLR, labelled _LR_ . Data on coastline length are provided by the CIA database (www.cia.gov); data on the fraction
a positive or insignificant relationship between capital controls and flows instead of a negative relationship. For instance, Forbes and Warnock (2012) find no significant relationship between capital controls and a country’s likelihood of experiencing a surge or stop in capital flows. Given the potential endogeneity of capital controls to capital flows, we do not include it in the specifications presented in this paper. Our main results for sovereign ratings and relative risk ratings, however, are robust to the inclusion of the Chinn-Ito index of capital controls (Chinn and Ito, 2008). These additional robustness results are available from the authors upon request.

13
being selected as beneficiaries of the program are left out of the program (Heckman, Ichimura and Todd, 1997). Given that this “overlapping support” condition is not satisfied by PROGRESA’s methods of selecting eligible households we are unable to apply this method and test its performance using the PROGRESA sample. See Diaz, Handa and Orozco (2003) for an analysis of the evaluation bias of matching estimators using comparison households outside the PROGRESA evaluation sample.

8
# Figure 8. ECA: Potential output growth and its drivers

<!-- Start of picture text --> A. Potential GDP growth<br>Percent<br>8 2000-10<br>2011-21<br>6 2022-30<br>2000-21<br>4<br>2<br>0<br>EMDEs ECA<br><!-- End of picture text --> <!-- Start of picture text --> A. Potential GDP growth B. Investment growth<br>Percent Percent<br>8 2000-10 14 ECA Median<br>2011-21<br>12<br>6 2022-30<br>2000-21 10<br>4 8<br>6<br>2<br>4<br>0 2<br>EMDEs ECA 0<br>2000-21 2000-10 2011-21<br>C. Labor force participation rate D. Working-age population growth<br>Percent of working age population Percent<br>3 ECA Median<br>80 Male Female<br>70<br>60 2<br>50<br>40<br>1<br>30<br>20<br>0<br>2000-2021 2000-2010 2011-2021<br>E. Share of population aged 30-34 years F. Quality-adjusted years of higher<br>with tertiary education education<br>Percent of population 30-34 Years<br>75 2.0<br>1.5<br>50<br>1.0<br>25<br>0.5<br>0 0.0<br>ECA CA CE EE RUS SCC TUR WBK ECA CA CE EE RUS SCC TUR WBK<br>ECA CA CE EE RUS SCC TUR WBK<br><!-- End of picture text --> Sources: European Commission; Eurostat; Penn World Tables; UN Population Prospects; World Bank, World Development Indicators database.

Note: CA = Central
achieved by focusing on a shorter time period, working with fewer markets, or tracking a very narrow basket. The statistics are thus not representative of the WFP data base, but represent the data selection of the paper which seeks to balance reasonable data coverage and data availability.

The raw price data of these selected food items remains a challenging source to deal with. Available data is regularly contaminated by outliers, which could be due to incorrect survey entries (e.g. a misplaced digit). Moreover, many local price series are incomplete and price quotes of different products may become available at different times and locations. The data availability constraints become more problematic when the interest is in tracking inflation, which requires prices quotes to be matched with historical quotes using a fixed time interval. For any given country, if the data selection was made such that the data coverage was 100%, the selection would collapse to zero cases unless the focus would be on an extremely narrow selection of data points that introduces strong sampling

> 10There may also be nonlinear patterns that carry important information about unobserved prices. First, a change in price ratio between two prices may be an indicator that a certain food of interest will likely trade against an elevated price ratio to some other food. Second, governments may use price controls and so certain prices may remain fixed for long periods. This can for example be observed in the bread prices in Afghanistan here (Andr ́ee, 2021). These fixed prices regularly break once input prices have risen too sharply, and such level shifts in the data can signal that all price levels must seek new price equilibria. To that regard, there may also be time-varying relations between various prices that, for example, change depending on key level shifts in the prices of certain foods.
By employing an interacted panel vector autoregression model, Huidrom et al. (2019) examine the effects of fiscal shocks not only on private consumption and CDS spreads (as a measure of risk premia) but also on output. The study finds that the relevance of Ricardian and investor sentiment considerations (i.e., private consumption and borrowing cost effects) as the two channels for a government’s fiscal position to matter for fiscal multipliers (i.e., output effects). Similarly, evidence points to less effective monetary policy in the presence of high debt because of poorly anchored inflation expectations in highdebt countries (Kose et al. 2019).

# 4.4. Slowing investment and growth

With higher debt typically comes higher debt service. Spending on higher debt service needs to be financed through some combination of increased borrowing, increased taxes, and reduced government spending. Spending cuts may even include spending on critical government functions such as social safety nets or growth-enhancing public investment (Figure 6.C and 6.D; Debrun and Kinda 2016; Obstfeld 2013; Reinhart and Rogoff 2010). Separately, high and rising government debt may raise long-term interest rates and yield spreads.<sup>7</sup> High debt could also create uncertainty about macroeconomic and policy prospects and weigh on growth (Lo and Rogoff 2015). It includes risks that the government may need to resort to distortionary taxation to rein in debt and deficits (IMF 2018; Kumar and Woo 2010). Higher interest rates and uncertainty would tend to crowd out productivityenhancing private investment and weigh on output growth. By using data on U.S. stock returns, Croce et al. (2018) find that an increase in the cost of capital for innovationintensive firms results in declines in productivity and economic growth. As a result, a rise in government debt is associated with a decline in corporate investment and R&D. Crowding-out of private investment due to high public debt is also shown in Huang, Pagano, and
to 80 percent of the explained variation in measurement error. Asymmetric rounding of selfreported plot areas at half-acre increments accounts for the second major source of measurement error, accounting for 18 percent of the explained variation. Further, focal point bunching around simple rounded values is highly asymmetric, with farmers more likely to round up than down, especially at smaller plot sizes.

Perhaps most strikingly, NCME persists within the treatment group in follow-up (postharvest) interviews conducted three to four months after the information treatment during the postplanting period. Even though plot size is directly observable and farmer subjects witnessed GPS measurement and were directly provided the true plot area and were informed of the error in their prior self-report of that variable, updating was far from complete. Further, farmers' updates of their self-reports are asymmetric. Upward corrections are more common than downward ones. The magnitude of updating in self-reported plot areas also varies by true plot area as well as with the magnitude and direction of the pre-treatment error in self-reported information. Finally, receiving information on true plot area and the extent of measurement error in self-reports affects subsequent farmer reporting on plot-level fertilizer and labor inputs, indicating that the effects of measurement error and updating spill over across variables and that survey data on non-land agricultural inputs may likewise contain significant NCME that correlates with the measurement error in plot area, with direct implications on the estimates of marginal returns to these inputs and a range of behavioral and policy parameters of interest.

We cannot identify the causal mechanisms behind these observations, which clearly merit further investigation. The absence of quick correction of demonstrably mistaken beliefs seems surprising given that rural households in Malawi depend heavily on land to generate income. Farmers have strong motives to know the true size of their plots. The fact that NCME is so widespread in such a readily observable, important input and that farmers learn and correct their errors incompletely and asymmetrically strongly signals likely behavioral anomalies that matter for (a) the design and implementation of information-based interventions that aim to ameliorate mistaken beliefs/persistent learning failures and
, Vol. 76, 2.

10. **Xiao and Xiao, Y.** Equity of Pension System in China,. _Shanghai Economic Research._ 2008, Vol. 8.

11. **Centre for Health Statistics and Information, Ministry of Health,.** An Analysis Report of National Health Services Survey in China in 2008. Beijing : Press for China Union Medical University, Dec 2009.

12. **Meng, Q.** Equity, Efficiency and Sustainability of Health Financing in China. _Health Economics Study,._ 2007, Vol. 4.

13. **OECD.** _OECD Health Data._ Paris : Organization of Economic Cooperation and Development, 2010.

14. **Chen, B.** Allocation Planning for Big Medical Equipment in China. _China Medical Equipment._ 2007, Vol. June.

15. **Ministry of Health, World Health Organization, UNICEF and UNFPA.** _Joint Review of Maternal and Child Survival Strategy,._ Beijing : In Press, 2006.

16. **Rudan, I*, et al.** Causes of deaths in children younger than 5 years in China. _The Lancet._ on behalf of WHO/UNICEF’s Child Health Epidemiology Reference Group (CHERG), 2010.

17. **Anand, S, et al.** China’s human resources for health: quantity, quality, and distribution. _The Lancet._ 2008;, Vol. 372, 1774 ‐ 1781.

18. **United Nations Development Programme, China Office.** _Human Development Report China 2007/2008 ._

[Online] http://unpan1.un.org/intradoc/groups/public/documents/un‐dpadm/unpan042714.pdf.

19. **Hu, SL.** The Implementation and Evaluation of the Rural Cooperative Medical Insurance Scheme. _China Health Economics._ February 2008.

20. **Brixi,
1.4 data 23

# 1.4.4 _Bilateral Investment Treaty_

Data on bilateral investment treaties between 30 OECD source countries and 87 IAB host countries are collected from UNCTAD’s Investment Instruments Online.<sup>23</sup> Bilateral Investment Treaties (BITs) are agreements between countries for the reciprocal encouragement, promotion and protection of investments in their territories. Like preferential trade agreements, BITs, too, have proliferated dramatically in recent decades from around 400 in 1990 to over 2,500 at present.

# 1.4.5 _Gravity and Other Variables_

The gravity variables – distance, past colonial relationship, contiguity and shared languages – are obtained from CEPII (2010). Economic variables – GDP, GDP per capita, average (weighted) applied tariff on manufactured imports – are from the World Development Indicators (WDI) database (World Bank 2011). Mean years of schooling are from Barro & Lee (2010), as intrapolated in UNDP (2010). These explanatory variables available annually are averaged over the preceding five years, that is, 2002 to 2006 to minimize the possibility of simultaneity. I compute the remoteness index as the sum of all bilateral distances between a country and all its partners, weighted by the share of the partner’s GDP in total world output.

With 30 source countries and 87 host countries, there are 2610 potential observations. Gravity-related information is not available for three host countries (Kosovo, Montenegro and Serbia). Because 15 OECD countries also appear in the IAB sample of FDI recipients, another 15 observations are lost, reducing the number of observations to 2505. If FDI stock data are missing for the years 2007-08, but they were reported for the period 2002-06, I recode the missing data points as zero.

23 See UNCTAD (2010).
schooling laws in the United States and find a positive effect: parental education decreases the probability of grade repetition and drop-out. Chevalier (2004) using a similar approach in the United Kingdom also finds a positive effect of parental schooling on their children’s education. But Black, Devereux, and Salvanes (2003) use reforms of the education system in Norway in the 1960s as an instrument and find no significant positive relationship between parental education and children’s schooling, except in the mother-son relationship.

Comparing adoptive children with biological children is the third strategy. By looking at the outcome of adopted children, genetic effects will disappear leaving only the environmental effect. Sacerdote (2002) uses a British sample of adoptees and finds a positive and significant relationship of parental education on the education outcomes of their children. Björklund and Richardson (2001) find no significant effect among foreign-born adoptees in Sweden. Plug (2004) analyzes outcomes for adoptees from Wisconsin and reports that, when both parents’ education is included in the regression, only the father’s coefficient remains significantly positive, while the mother’s loses its significance. Björklund, Lindahl, and Plug (2004), using data from Sweden, confirm the validity of the adoption strategy and find that

> 1 Notice that Antonovics and Goldberger (2003) question the robustness of their results.

3
, transportation equipment, public transportation) good/service categories. This classification is, in part, informed by the implicit carbon intensity of nonfuel goods/services (i.e., goods/services with similar carbon intensities are classified under the same category).

> 21 This assumption is less realistic for Emissions Intensive Trade Exposed (EITE) industries competing in world markets with limited ability to pass cost increases forward to final consumers.

> 22 See: https://www.gtap.agecon.purdue.edu/databases/v10/index.aspx. Given that the GTAP-10 input-output tables are relatively outdated, they are scaled such that their implicit revenue flows from a hypothetical climate mitigation policy match those of CPAT (which are primarily based on IEA (2021) data).

23 These cover the following five fossil fuels: coal (‘coa’), electricity (‘ely’), oil (‘oil’), natural gas (‘gas’, ‘gdt’) and petroleum products (‘p_c’).

11
income countries and mostly uses information on inequality backed out from distributional national accounts rather than from observed consumption vectors, which is less relevant for our purposes.

In the appendix, we expand the scenario analysis by looking at different combinations of inequality changes and energy and carbon intensity, and we further vary other key modelling parameters: the population growth forecasts until 2050 and passthrough rates from GDP growth to consumption growth. Population scenarios are based on the low, medium, and high variant of the UN’s Population Prospects (United Nation 2022). For the passthrough rates from GDP growth to consumption growth, we use the 10<sup>th</sup> and 90<sup>th</sup> percentile of the distribution from Extended Figure 4, Panel A for all countries. In total, we analyze 243 scenarios, which are outlined in Extended Table 4.

# **Extended Table 4. Parameters in different model scenarios**

|**Parameter**||**Interpretation**|**Value**|
|---|---|---|---|
||High|90<sup>th</sup>percentile of changes in<br>inequality (increase)|+13% in Gini in 2050|
|Inequality|Baseline|No change<br>|0%|
||Low|10<sup>th</sup>percentile of changes in<br>inequality (decline)|-17% in Gini in 2050|
||High|Less growth passes through to<br>consumption|0.44%<br>increase<br>in<br>consumption<br>for<br>1%<br>growth in GDP|
|Passthrough rate of GDP<br>growth to consumption|Baseline<br>Low|Country-specific<br>estimate<br>of<br>passthrough rate<br>More growth passes through to
two appealing features. First, towns along the inner border of the quake region suffered minor damages or none at all, as reported by the government agencies assessing the consequences of the quake. Specifically, of the 57 towns, 18 did not record any damage, 15 towns ranked at the very lowest level of the damage scale, meaning only very mild and limited damage was

> 7 Town sizes are computed as of 1979 from Istat population records.
being budget leakages which also require calculating how much money was _supposed_ to reach a given health facility.<sup>8</sup> A country with high marks on all five of these governance performance measures has a functioning public health care service, while a country with low marks does not. Thus, independent of any effort to gather information on governance determinants across countries, these five governance performance measures could be used to benchmark the performance of public health care provision across countries and identify areas that are problematic. If worker absenteeism is high, then the personnel management system needs to be investigated. If counterfeit drugs are common, drug regulations and enforcement need analysis.

Additional governance performance measures that appear in different studies and which could also be considered include:

- share of births and deaths that are registered (HSM )

- disparities in health care service coverage between high and low income groups (HSM)

- share of health facilities that receive supervisory visits in accordance with national guidelines (HSM)

- share of workers who complete programmed training (GFATM)

- share of health facilities that are staffed according to national standards (GFATM, Tanzania)

- share of budgeted funds that are executed (PEFA, GFATM)

- share of positions that are vacant (GFATM SWEF)

- share of audited objections settled within expected time frame (Bangladesh)

- share of contracts awarded within initial bid validity period (Bangladesh)

- share of nationally distributed commodities that reach local facilities (Bangladesh)

- share of local governments submitting health information reports on time (Ethiopia, HSDP II, Mozambique and Tanzania)

- share of donor agency funds that are disbursed through the federal Ministry of Health without earmarking (Ethiopia, HSDPII and Ghana)

- share of maternal deaths that are audited (Ghana and Nicaragua)

- share of population aware of their rights to public health care services (Kyrgyz Republic)

- share of providers with satisfactory compliance of management agreements (Nicaragua)

- share of providers complying with medical protocols (WHO and IHME)

- share of
.001)|(0.001)|(0.006)|(0.005)|(0.009)|
|Year FE|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Firm FE|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Observations|209150|3004|7465|20671|48001|12016|24032|77959|17504|
|R‐squared|0.029|0.245|0.256|0.096|0.024|0.035|0.053|0.056|0.071|

Note: Non‐compliance=1. Firm size, firm age, baseline non‐compliance, and length of participation in program are all identified as a dummy variable equal to 1 if the firm is above the median. Standard errors in parentheses. * = 10% significance, ** = 5% significance, *** = 1% significance. Source: Author’s calculations using data from ILO‐IFC Better Work Vietnam.

27
. The index was then normalized, that is, norm_index = ((index ‐ min(index)) / (max(index)‐min(index))). Household average years of education is the numbers of years of education completed by each individual collapsed to the household mean number of years of education. This variable also was normalized. The wealth and education index displayed in Table 1.4 is the sum of the normalized wealth index and the normalized average number of years of education.

**c.** The following variables were used to create an agricultural asset index with principal component analysis: Hand hoe dummy, watering can dummy, hand tool dummy capturing whether the household had any of (slasher, axe, sprayer, panga, or sickle), a dummy capturing whether the household had any of (tractor, tractor plough, ridger, cultivator, treadle pump, motorized pump, ox cart, or ox plough), livestock facility dummy capturing whether the household had any of (chicken house, livestock kraal, poultry kraal, or pig sty), and grain storage dummy capturing whether the household had any of (storage house, granary, or barn). The index was then normalized, that is, norm_index = ((index ‐ min(index)) / (max(index)‐min(index))). Land holdings are the number of hectares (ha) that the household “holds.” Plots that the household farmed on and had acquired through being granted by local leaders, inheritance, bride price, purchase, or leasehold (about 5% of all plots were acquired by means other than these) are said to be “held.” Any plots that the household did not farm but received rent for also were counted as being held. This variable then was normalized. The agricultural and land index displayed in Table 1.4 was the sum of the normalized agricultural asset index and the normalized household land holdings.

Overall households in Southern
13

## TABLE 2

# TRANSFERS AS A PERCENT OF RECIPIENT GOVERNMENT EXPENDITURES, SELECTED COUNTRIES 1974 AND 1984

|Country and<br>ofGovern|Lexel<br>ment|1974|1984|Increase (+) or<br>Decrease(-)|
|---|---|---|---|---|
|Bolivia|State|na|8.9|na|
||Local|na|43.3|na|
|Brazil|State|19.6<br>|16.5||
||Local|60.5|62.8|+|
|India|State|32.7|3 3 <sup>.</sup>0 <sup>b</sup>|+|
|Indonesia|State|8 1 <sup>.</sup>3|95.9|+|
|Kenya|Local|22.6|46.3|+|
|Korea|State|50.0|na|na|
||Local|81.7|na|na|
|Malawi|Local|45.0|26.8|-|
|Mauritius|Local|67.2|84.8|+|
|Philippines|Local|na|41.7|na|
|Thailand|Local|71.8|43.2|-|
|Tunesia|Local|33.9|42.1|+|
|Uruguay|Local|12.3|8.5|-|
|Venezuela|Local|55.9|na|na|

aThe term "state" is used to refer to an intermediate level of government, e.g., province or state.

1983 data.

01975 data.

1981 data.

SOURCE: International Monetary Fund, Government Finance Statistics Yearbook, 1984 and 1986 (Washington, DC: IMF, 1984 and 1986).