rafmacalaba/datause-encoder-data huggingface dataset:
active saving likely plays a significant part: household income growth starts to outpace household consumption growth around that time, and the saving rate increases by 20 percentage points between ages 40 and 65. This suggests that programs that aim to encourage formal saving may be most successful among individuals in that age range.

# **References**

Attanasio, Orazio and Steven Davis, “Relative wage movements and the distribution of consumption,” Journal of Political Economy, 1996, 104 (6), 1227–1262.

Attanasio, Orazio and Guglielmo Weber, “Consumption and saving: models of intertemporal allocation and their implications for public policy,” Journal of Economic Literature, 2010, 48 (3): 693-751.

Banks, James, Richard Blundell, and Sarah Tanner, “Is there a retirement-savings puzzle?,” American Economic Review, 1998, 769–788.

<mark>Bonnet, Florence, Joann Vanek, and Martha Chen, “Women and men in the informal economy: A statistical brief,” International Labour Office, Geneva, 2019.</mark> <mark>Cagetti, Marco, “Wealth accumulation over the life cycle and precautionary savings,” Journal of Business & Economic Statistics 21.3, 2003, 339-353.</mark> Carroll, Christopher D, “Buffer-stock saving and the life cycle/permanent income hypothesis,” The Quarterly journal of economics, 1997, 112 (1), 1–55.

___ and Lawrence H Summers, “Consumption growth parallels income growth: Some new evidence,” in “National saving and economic performance,” University of Chicago Press, 1991, 305–348.

Cho,Yoonyoung and Zaineb Majoka, “Pakistan jobs diagnostic : promoting access to quality jobs for all,” Washington, D.C. : World Bank Group, 2020, Vol. 1 of 2.

Deaton, Angus, “The analysis of household surveys: a microeconometric approach to development policy,” The World Bank, 1997.

Deaton, Angus, “Saving and liquidity constraints,” Econometrica, 1991, 59 (5), 221–248.

___ and Christina Paxson, “Saving, growth, and aging in Taiwan,” in “Studies in the Economics of Aging,” University of Chicago Press, 1994, 331–362.

___ and Christina Paxson, “Growth and saving among individuals and households,” Review of Economics and Statistics, 2000, 82(2), 212-225.

20
WPS3784 These average gains are substantial and raise the question of why farmers are not already making this shift if there is a strong incentive in terms of higher incomes. There must be constraints which are preventing farmers in rural areas from moving away from subsistence agriculture even though they have a propensity to produce coffee. There are a number of issues here that must be addressed including the costs that are incurred in shifting into coffee, in terms of the investment in plants and so on, the lack of income whilst the new plants become harvestable, and the higher risks associated with cash crops. Cadot et al (2005) suggest that these adjustment costs can be very large. They estimate that the cost of moving out of subsistence for farmers in Madagascar can amount to around 250 percent of the value of a subsistence farmer’s annual production. These costs and investments all necessitate the effective provision of credit to rural communities, something which is severely lacking at present. In addition, there is also the need for information on techniques for producing different crops, and so a need for widely available extension services, as well as access to transport.

16
# **Biodiversity and National Accounting**

# Kirk Hamilton<sup>1</sup>

# **Introduction**

The absence of any valuation of the depletion and degradation of natural resources and the natural environment in the System of National Accounts (SNA) leads at best to policy complacency and at worst to policy mistakes. As _The Changing Wealth of Nations_ (World Bank 2011) documents, the effect of this absence is likely to be felt most keenly in developing countries, where natural resources and the natural environment constitute 21%-35% of total wealth.

Ignoring the consumption of all forms of capital flatters the growth rates of gross domestic product (GDP) in all countries, which can be a powerful source of complacency. In poor countries that are highly dependent on exhaustible natural resources, the combination of fiscal policies and resource sector policies can result in gross wealth creation that is positive and growing, while the net change in produced plus natural wealth, unmeasured in the SNA, is negative – this is a policy mistake of the first order.

As a large literature attests, and as will be reviewed below, biodiversity is a source of economic benefits. Most of these benefits are currently measured in the SNA. The problem, and a key source of policy errors, is that there is no _explicit_ measure of these benefits. As a result, development decisions guided by the traditional indicators of the SNA, such as GDP growth, can harm natural areas and the biodiversity which they harbor, leading to unforeseen losses of wellbeing. Policy surprises abound.

For commercial natural resources, the adoption of the System of Environmental-Economic Accounting (SEEA) as a United Nations statistical standard (United Nations 2012) is a major step forward in defining the contribution of natural resources to national income, including the effects of depleting these resources. From a biodiversity perspective this work is still incomplete, however, because work on accounting for ecosystem services, in particular, is ongoing.

This paper explores the extent to which biodiversity and changes in biodiversity could be measured within the existing structure of the SNA, as well as assessing how the SNA could be extended to include a broader portion of the value of biodiversity. We begin by summarizing the treatment of natural resources in the national balance sheet account, and review key portions of

> 1 Kirk Hamilton is Lead Economist, Development Research Group, The World Bank. The comments of Dieter Helm, Colin Mayer, Ed Barbier, Giles Atkinson and Mike Toman are acknowledged with thanks.  The research and conclusions presented are solely the author’s and should not be attributed to the World Bank, its Executive Directors, or governments of World Bank member countries.

2
_Annex 2: Technical Efficiency by Country (1999–2009)_ <!-- Start of picture text --> 100%<br>90%<br>80%<br>70%<br>60%<br>50%<br>40%<br>30%<br>20%<br>10%<br>0%<br>Source: Own Calculations<br>Technical Efficiency<br><!-- End of picture text --> _Annex 3: Comparison between Data Envelopment Analysis and Stochastic Frontier Analysis Results_ We calculated the technical efficiency frontier for 2009 using Data Envelopment Analysis (DEA) under two different specifications, constant returns to scale (CRS) and variable returns to scale (VRS). In DEA, the output variable is annual container throughput and the input variables are (i) length of berths (in meters), (ii) terminal area (in square meters), and (iii) crane capacity equivalent. The latter variable is a combination of the number of ship-to-shore (STS) gantry cranes and mobile cranes, in which the number of STS gantries is estimated as a mobile crane equivalent. This approach allows maximizing the number of observations included in the estimations; since many ports would drop from the sample due to nil values in the variables STS cranes or mobile cranes. Overall, 62 container terminals were included in the estimations, compared to 67 in the SFA. Moreover, it’s important to highlight that under the DEA specification it’s not possible to account for the use of ships’ cranes and transshipment as binary variables as in the SFA.

# a) Constant Returns to Scale

Under constant returns to scale, DEA produces the results showed in the table below. The distribution of technical efficiency according to these results has an average of 40% and a standard deviation of 27%, similar to the statistics obtained with SFA (41% and 21%, respectively).

We found a positive correlation of 0.62 when comparing the technical efficiency rankings obtained with the SFA and DEA-CRS. Culinnane et al. (2006a) have already provided evidence that the two methodological approaches produce analogous results: the authors found a correlation of 0.79 when applying similar input/output specifications for SFA (truncated normal distribution) and DEACRS. Among the sources of difference from our estimations, it’s important to highlight that the estimation strategy we used for SFA is different, by controlling for transshipment and ships’ cranes use, on top of other control variables used in the production function estimation. In spite of this, only six out of fifty-four ports had a difference larger than 30 efficiency points between the results obtained through both approaches, as shown in the scatter plot below.

26
68. A sample questionnaire for assessing liquidity risks, prepared by the National Bank of Belgium, and a format for liquidity reports required by the OCC are presented in Annex II. Annex III shows a possible format for a Contingency Funding Plan (also from the OCC).

69. The first step in designing a CFP is to define severe but plausible **_stress scenarios_** . Stress testing allows the bank to analyze liquidity risks based on the performance of different liquidity risk drivers and variables under distressed circumstances. The stress testing, therefore, allows the bank to define in its CFP the liquidity needs that it may face under stress situations, tests the adequacy of liquidity buffers to ensure its ability to meet payments in stressed situations, and design the activation of mitigation actions.

70. The stress testing begins with the construction of scenarios. The scenarios can simulate idiosyncratic problems that only affect the entity, systemic problems that affect the banking system as a whole, or combinations of the two<sup>58</sup> . The construction of the scenarios involves the definition of risk events associated with the bank’s activities, products, funding sources, and markets. It can also incorporate a historical analysis of the events (when available), and the analysis of the impact and risk under each of them. The objective of stress testing is to:

- Detect the vulnerabilities of the bank.

- Check if the bank’s risk exposure falls within the risk tolerance defined by the bank’s Board.

- Develop an effective CFP which contains the actions and measures to face the liquidity problems, under each scenario, based on the nature of the problems, probability of occurrence and intensity of shocks.

71. The liquidity scenarios should not focus only on expected and unexpected cash flows but also on asset liquidity, since most institutions rely on generating liquidity from securities positions. They should include<sup>59</sup> :

- Temporary disruption in liquidity when funding is required only for a short time--for example, an operation breakdown, a physical emergency, the withdrawal of a major player, etc.

- Longer term distressed environments: interbank market difficulties, distress in specific currencies, tightening credit lines.

- At least one scenario in which the bank is no longer considered to be investment grade.

- A sudden change in the composition of deposits, a sudden increase in deposit withdrawals, the impossibility of selling an asset within a given time horizon

72. The CFP should contain: a definition of the events triggering the plan, a description of the potential sources of funding on boththe asset and liability sides, an escalation procedure detailing how additional funds could be raised, and a procedure for the management of the plan.

> 58 CEBS,, page 46

> 59 OCC, Handbook, page 37
EU and some non-WTO members, I include a dummy variable (quota) equal to unity only for countries subject to such quotas.'? Two other dummies (atc2 and atc3) are equal to unity for years 1998 onwards and 2002 onwards, in order to capture the effects of quota liberalization under phases 2 and 3 of ATC implementation.!* I also interact these dummies with quota to allow for differences in impact across quota and non-quota countries.

180 t st Beaters: + Bratc3s + Bgquotazs + Boquotdrs * atc2., + B gquotdrs * atc3s: + Exse (15) Baseline results using OLS appear in the first column of Table 3.!° The overall fit of the model is good, with R? = 0.68. All of the substantive coefficients carry the expected signs: GDP and per capita GDP are both positive, while tariffs are negative. This is in line with previous work, although these coefficients are not statistically significant in this case. Countries subject to ATC quotas tend to export a narrower range of varieties, and this effect is statistically significant at the 1% level. The impacts from ATC liberalization are as expected in terms of sign (positive for quota countries, negative for the others), but are not statistically significant.

In line with the model’s predictions, the total standards count enters with a negative coefficient, while the extent of international harmonization is strongly positive. Both are statistically significant at the 1% level. In terms of magnitude, a 1% increase in the total number of EU standards is associated with a 0.8% decrease in export variety, while an increase of one percentage point in the proportion of those standards which are internationally harmonized is associated with a 0.02% increase in export product variety. While this latter effect is quite small, I would argue that it '7Countries considered to be subject to quotas over the 1995-2003 period are: Argentina, Belarus, Bosnia and Herzegovina, Brazil, China, Hong Kong China, India, Indonesia, Macao China, Malaysia, North Korea, Pakistan, Peru, the Philippines, Serbia and Montenegro, Singapore, South Korea, Sri Lanka, Taiwan China, Thailand, and Vietnam. (Source: WTO Trade Policy Reviews of the European Union, various years.) '8T assume that ATC quotas apply only to the textiles and clothing sectors as defined here, and not to footwear. This is basically consistent with the product list in the Annex to the ATC, which has extensive coverage in HS chapters 50-63, but lists only three 6-digit product lines in Chapter 64. '9Since the two standards measures vary in the sector-year dimension, the robust standard errors are adjusted for clustering at that level.

18
worth about 14 (±6) days of independent production, are lost during disasters and cannot be recovered within a two-month period (Table 6).<sup>4</sup>

**Table 6: Regression results for the impact of disaster risk perception and disaster experience on input inventory sizes**

|||In|put inventory|size||
|---|---|---|---|---|---|
|VARIABLES|(1)|(2)|(3)|(4)|(5)|
|**Direct disaster risk perception**(no risk)||||||
|Low risk|1.445||||1.096|
||(6.534)||||(6.793)|
|Moderate risk|-4.665||||-5.549|
||(4.546)||||(6.345)|
|High risk|-7.566||||-8.019|
||(5.583)||||(8.678)|
|Very high risk|-6.212||||-11.06|
||(6.197)||||(10.78)|
|**Indirect disaster risk perception**(no risk)||||||
|Low risk||-3.167|||-1.208|
|||(4.780)|||(4.789)|
|Moderate risk||-3.540|||1.777|
|||(4.310)|||(5.570)|
|High risk||-3.917|||5.493|
|||(4.588)|||(7.775)|
|Very high risk||5.054|||14.37|
|||(8.588)|||(13.25)|
|**Experienced flooding on business premises**||||||
|Yes|||-7.196***||-5.817*|
||||(2.660)||(2.983)|
|Longest supply delivery delay in days||||-0.00785|-0.00369|
|||||(0.0624)|(0.0694)|
|**Time since last serious disaster**(>12 months)||||||
|<2 month|-14.73**|-10.66|-12.99*|-14.16**|-14.86**|
||(6.059)|(6.565)|(6.743)|(6.559)|(5.869)|
|2–12 months|-1.026|-0.131|-0.697|-2.096|-1.090|
||(3.692)|(3.578)|(3.793)|(3.730)|(3.196)|
|Fixed effects|Yes|Yes|Yes|Yes|Yes|
|Controls|Yes|Yes|Yes|Yes|Yes|
|Observations|576|576|574|558|556|
|R-squared|0.078|0.076|0.075|0.060|0.077|

_Note:_ Fixed effects include region-and sector-specific fixed effects. Input inventory sizes are measured as the maximum number of days that a firm can sustain current firm activity with its current stock of inventories. Controls include age of the firm, number of employees and log operational costs. “Time since last serious disaster” refers to whether the most recent disaster that a firm experienced was up to 2 months, 2–12 months or more than 1 year ago. The variable flood experience indicates whether firms have experienced flooding on their business premises Standard errors (in parentheses) are clustered on the ward level:<sup>*</sup> p<0.1<sup>**</sup> p<0.05<sup>***</sup> p<0.01

> 4 All results presented here should be interpreted with caution as variations in input inventory sizes across firms are large and regressions explain only a small fraction in the overall variance. Even after controlling for a range of fixed effects and other potentially confounding factors, the R<sup>2</sup> for most regressions lies between 6 and 10 percent. This indicates that other firm characteristics explain inventory size of firms or that self-reported answers are subject to large uncertainties.

12
Cohodes, S. R., & Goodman, J. S. (2014). Merit Aid, College Quality, and College Completion: Massachusetts’ Adams Scholarship as an In‐Kind Subsidy. _American Economic Journal: Applied Economics_ , _6_ (4), 251–285.

- Constantine, J. M., Seftor, N. S., Martin, E. S., Silva, T., & Myers, D. (2006). _Study of the Effect of the Talent Search Program on Secondary and Postsecondary Outcomes in Florida, Indiana and Texas. Final Report from Phase II of the National Evaluation_ . ED Pubs.

- Cunha, J. M., Miller, T., & Weisburst, E. (2018). Information and College Decisions: Evidence From the Texas GO Center Project. _Educational Evaluation and Policy Analysis_ , _40_ (1), 151–170.

- Dearden, L., Fitzsimons, E., & Wyness, G. (2014). Money for nothing: Estimating the impact of student aid on participation in higher education. _Economics of Education Review_ , _43_ , 66–78.

- Deming, D., & Dynarski, S. (2009). _Into College, Out of Poverty? Policies to Increase the Postsecondary Attainment of the Poor_ (Working Paper No. 15387). National Bureau of Economic Research.

- Denning, J. T. (2017). College on the Cheap: Consequences of Community College Tuition Reductions. _American Economic Journal: Economic Policy_ , _9_ (2), 155–188.

- Denning, J. T. (2018). Born Under a Lucky Star: Financial Aid, College Completion, Labor Supply, and Credit Constraints. _Journal of Human Resources_ .

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- Domina, T. (2009). What Works in College Outreach: Assessing Targeted and Schoolwide Interventions for Disadvantaged Students. _Educational Evaluation and Policy Analysis_ , _31_ (2), 127–152.

- Dunlop, E. (2013). _What Do Stanford Loans Actually Buy You?‐The Effect of Stanford Loan Access on Community College Students_ (CALDER Working Paper No. No. 94).

- Dynarski, S. (2000). Hope for Whom? Financial Aid for the Middle Class and Its Impact on College Attendance. _National Tax Journal_ , _53_ (3), 2.

- Dynarski, S. (2003). Does Aid Matter? Measuring the Effect of Student Aid on College Attendance and Completion. _The American Economic Review_ , _93_ (1), 279–288.

- Ehlert, M., Finger, C., Rusconi, A., & Solga, H. (2017). Applying to college: Do information deficits lower the likelihood of college‐eligible students from less‐privileged families to pursue their college intentions?: Evidence from a field experiment. _Social Science Research_ , _67_ , 193–212.

- Elsayed, M. A. A. (2016). The Impact of Education Tax Benefits on College Completion. _Economics of Education Review_ , _53_ , 16–30.

- Erikson, R., Goldthorpe, J. H., Jackson, M., Yaish, M., & Cox, D. R. (2005). On class differentials in educational attainment. _Proceedings of the National Academy of Sciences of the United States of America_ , _102_ (27), 9730–9733.

37
in urban areas but not rural ones, reflecting the likely informal nature of food purchases in rural areas. For these three reasons, the greater rural poverty increase is driven by the underlying welfare distribution and not modeling characteristics.

Figure 12: Fiscal policy’s impact on poverty headcount ratio by area of residence <!-- Start of picture text --> 3.0<br>2.5<br>2.0<br>1.5<br>1.0<br>0.5<br>0.0<br>Full system Direct Indirect<br>interventions interventions<br>Cambodia Phnom Penh Other Urban Rural<br>Percentage point change<br><!-- End of picture text --> Source: Authors’ calculations based on CSES 2019/20 and fiscal data. Note: Pre-fiscal poverty headcount; Cambodia=17.7%, Phnom Penh=4.1%, Other urban = 12.4%, Rural = 22.6%.

Table 4: Fiscal policy’s impact on inequality and poverty by area of residence

||Market income|Disposable<br>Income<br>Con<br>inco|sumable<br>me|Final<br>income|
|---|---|---|---|---|
|||Gini coefficient (%)|||
|Cambodia|32.4|32.2|32.2|31.4|
|Phnom Penh|34.8|34.5|34.6|34.0|
|Other Urban|30.9|30.8|30.9|30.2|
|Rural|28.0|28.0|28.0|27.5|
|||Poverty headcount ratio (%)|||
|Cambodia|17.7|17.8|19.8|n.a.|
|Phnom Penh|4.1|4.2|5.0|n.a.|
|Other Urban|12.4|12.6|14.3|n.a.|
|Rural|22.6|22.8|25.1|n.a.|

Source: Authors’ calculations based on CSES 2019/20 and fiscal data.

# **Fiscal Impoverishment and Fiscal Gains to the Poor**

Comparisons of poverty before and after taxes and transfers are applied only broadly indicates the gains or losses the fiscal system creates. But this can fail to capture an important aspect: that some poor are made poorer (or non-poor are made poor) by the tax and transfer system. Fiscal Impoverishment (FI) traces the number of people who are impoverished following the execution of the fiscal policy.

20
<mark>economic and living conditions of a sample of Syrian refugees living in the Al Azraq and Zaatari camps and refugees and Jordanian citizens living in the surrounding governorates.</mark> <mark>I retrieved the following data from the survey: characteristics of the refugees' household head, household income per capita, household dwelling, access to services such as electricity and water, assets accumulation, and overall life experience. The survey also has some</mark> retrospectiv <mark>e information on pre-crisis characteristics, such as t</mark> he household head's economic status in Syria, household earnings in 2010 before the crisis, household assets in Syria, and the number of years the household has been living in Jordan. The estimations in this paper use this information as control variables.

# 4.2. Identification strategy

Using an immigration survey to evaluate causal impacts often faces identification threat (Borjas, 2018). In other words, refugees who are surveyed in camps may be different from refugees who are surveyed in the cities, leading to selection bias. I identify three reasons selection bias can arise in this research. Refugees may self-select to live outside of the camp because they:

- (a) have social networks in the cities who can accommodate them.

- (b) have more access to finance to pay for the rent, or

- (c) because of their economic desire for access to jobs (Malaeb & Wahba 2019).

I confront refugees' self-selectivity by employing the difference-in-difference (DiD) method and the propensity score matching method. The DiD method is used to estimate the effect of a given instrument on a treatment group and a control group over time, attributing any differences to the effect of treatment membership (Athey & Imbens, 2006; Lechner, 2010). While the DiD controls for baseline differences in the outcome of interest, it holds a parallel trend assumption, for which it is difficult to account. This parallel trend assumption requires that the difference between the treatment and control group should be constant over time in the absence of the treatment. However, it is possible that some factors could change the composition of either the treated or control group over time. Following Gibson & McKenzie (2014) and Stuart et al. (2014), I combine the DiD with the propensity score matching method to increase the likelihood of the parallel trend assumption holding.

10