rafmacalaba/datause-encoder-data huggingface dataset:
effect or the demand-supply mismatch exists among younger workers, it has been more than offset by other forces. On the other hand, the evidence suggests that there is a process that negatively affects older workers, those ages 50 years and older in particular. Older workers, for example, do not feature the typical continuously increasing concave wage function; rather, they experience a sharper decline in their wage. These findings are summarized in Table 5. We also observe that the age at which the slope becomes negative occurs at younger ages with each cohort (Table 3 and Figure A7 in the Appendix). This could be evidence of an accelerated skillsobsolescence effect. However, the year at which the wage becomes negative for the collegeeducated of every cohort coincides with the year in which Mexico entered into recession: 2008.

**Table 5: Summary of Evidence**

|**Hypothesis**|**Shown**<br>**in**|**Degraded tertiary**|**Obsolescence / Labor**<br>**Saving**|
|---|---|---|---|
|Datt-Ravallion|Table 2|No evidence|Not applicable|
|Evolution of wages<br>by age|Figure 4|No evidence|Some evidence that older<br>workers’ salaries fell more<br>rapidly|
|Wage inequality by<br>age|Figure 5|No evidence|Evidence consistent with<br>high-wage older workers ́<br>wages falling relatively to<br>other older workers’ wages|
|Evolution of wages<br>by cohort|Table 3|Some evidence with<br>the very young<br>cohorts but it overlaps<br>with year of<br>macroeconomic crisis|Evidence that the wages of<br>older workers decline|
|Cohort regression|Table 4|No evidence. On the<br>contrary, the average<br>entry-point wage has<br>been increasing over<br>time for the younger<br>cohorts|Evidence that the wages of<br>older workers decline in the<br>more recent cohorts and<br>that this accelerates with<br>how recent the cohort is.|

# **5. Main Findings and Conclusions**

This paper examined whether there is evidence of a degraded tertiary education effect, a skillsobsolescence effect, or both in explaining the decline in the relative wages of skilled workers in 12
Sources: Notre Dame Global Adaptation Initiative; Penn World Tables; Portulans Institute; UN Population Prospects; World Economic Forum; World Bank; World Development Indicators database. Note: Estimates based on production function approach.

A. Portulans Institute Network Readiness Index estimates preparedness to benefit from emerging technologies and capitalize on the opportunities presented by the digital transformation; higher value indicates better readiness. Unweighted group averages.

B. The Notre Dame Global Adaptation Initiative index reflects vulnerability to climate change and other global challenges in combination with the readiness to improve resilience. A higher value indicates lower vulnerability and/or better readiness. Sample includes 146 EMDEs; last observation 2019.

D.-F. Sample includes 53 EMDEs (14 from SSA). Period average of annual GDP-weighted averages.

# References

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Abreha, K., E. Lartey, T. Mengistae, S. Owusu, and A. Zeufack. 2020. “Africa in Manufacturing Global Value Chains. Cross-Country Patterns in the Dynamics of Linkages.” Policy Research Working Paper 9439, World Bank, Washington, DC.

Acemoglu, D., and P. Restrepo. 2017. “Low-Skill and High-Skill Automation.” MIT Department of Economics Working Paper 17-12, Massachusetts Institute of Technology, Cambridge, MA.

Acerbi, M., M. Heger, H. Naber, and L. Sieghart. 2021. “Middle East and North Africa: Two Opportunities for Rebuilding after COVID-19 in Green and Inclusive Ways.” Arab Voices (blog). November 22, 2021. https://blogs.worldbank.org/arabvoices/middleeast-north-africa-two-opportunities-rebuilding-after-covid-19-green-inclusive.

Acharya, D., and Y. N. Raju. 2020. “Revisiting the Volatility-Growth Relationship: Some Cross-country Evidence, 1978-2017.” Cogent Economics and Finance 8 (1): 1826655.

ADB (Asian Development Bank). 2016. Asian Development Outlook 2016: Asia’s Potential Growth. Manila: Asian Development Bank.

ADB (Asian Development Bank). 2022. “ADB Briefs: How to Recover Learning Losses from COVID-19 School Closures in Asia and the Pacific.” Asian Development Bank, Manila, Philippines.

Agarwal, R., V. Balasundharam, P. Blagrave, R. Gudmundsson, and R. Mousa. 2021. “Climate Change in South Asia: Further Need for Mitigation and Adaptation,” IMF Working Paper 21/217, International Monetary Fund, Washington, DC.

Alberola, E., R. Gondo, M. Lombardi, and D. Urbina. 2016. “Output Gaps and Policy Stabilization in Latin America: The Effect of Commodity and Capital Flow Cycles.” BIS Working Paper 568, Bank for International Settlements, Basel.

Alkhareif, R. M., W. A. Barnett, and N. A. Alsadoun. 2017. “Estimating the Output Gap for Saudi Arabia.” International Journal of Economics and Finance 9 (3): 81-90.
by more than the household’s marginal propensity to save from temporary income, that is _−_ MPBY _t,t′ ≥−_<sup><u>1</u></sup><sup>_−_</sup> MPC<sup><u>MPCMPC</u></sup><sup>_t,t′_when</sup><sup>_t′≤t_.Insummary,thisyields</sup> Second, I bound _Gt,t′ ∈_ [1 _,_ 1 _._ 5], with 1.5 corresponding to the ratio of Year 2-to-Year 1 loan amount in the low interest rate treatment group.

Using a one year MPC of 0.5, this yields These bounds on income effects are much wider than the bounds on static income effects in – Section 4.1 and 4.2 this is because the price reduction results in income effects from past, current, and future periods. Despite this, these bounds remain relatively tight compared to the demand elasticities estimated in Table 2. For the estimated Year 1 price elasticity of loan amount of _ε_ 1 = 6 _._ 4, the bounds on the compensated price elasticity of loan amount are _φ_ 1 + _ε_ 1 _∈_ [4 _._ 4 _,_ 6 _._ 9], while for the estimated Year 3 price elasticity of loan amount of _ε_ 3 = 14 _._ 1, the bounds on the compensated price elasticity of loan amount are _φ_ 3 + _ε_ 3 _∈_ [11 _._ 7 _,_ 15 _._ 0]. As in the previous two examples, statistical uncertainty is larger than potential bias from using uncompensated price elasticities as a measure of compensated price elasticities.

**Marshallian surplus from and equivalent variation bounds for low interest rate treatment and microfinance** I apply Equations 14 and 15 for Marshallian surplus and equivalent variation, respectively, to calculate the welfare gains from the low interest rate treatment and from the microfinance lending product.

The low interest rate treatment, expressed per $100 of control group loan amount, generated Marshallian borrower surplus of $2.1 in Year 1, with a standard error of $0.1, and $2.3 in Year 3, with a standard error of $0.1. This estimate is much more precise than – the estimated demand elasticity the interest rate variation induced by the experiment is sufficiently small that the impacts of the low interest rate treatment on borrower welfare are well approximated by the increased loan amount savers can receive holding fixed their repayments. Closely related to this point, bounds on equivalent variation to the low interest rate treatment are not meaningfully different from Marshallian surplus.

For larger price changes, the estimated elasticity plays a much larger role; Marshallian borrower surplus is simply 1 _/ε_ , implying that the microfinance lending product generates borrower surplus of $15.6 per $100 of deposits in Year 1, with a standard error of $7.8, and 35
Petroleum Corporation (NNPC) based on public data on oil production, global oil prices, standard cost estimates and cost patterns from outdated accounts of the company. The then Managing Director of NNPC was so interested that he spent four hours discussing the financial model. In no other case was a person at this level willing to spend that much time discussing substantive issues without even a hint of a deal on the table.

While comprehensive accounts are problematic for corrupt practices, some form of monitoring is needed on whether bribers paid and whether bribes received their agreed share. When IMF staffers once probed the fiscal accounts of Nigeria they could not reconcile numbers. When pressing for explanations, they were - allegedly - in the end able to see an accounting of major expenditures maintained at the very center of government in an exercise book. While this is no way to keep professional accounts, it might be a rational method for keeping track of corrupt payments. Such informal accounting need not be as comprehensive as full accounts, but just needs to track critical payments. It need just be used for monitoring of corrupt agreements not for managing a company or government finances.

# **A Country Case: Nigeria**

What might be the overall outcome of a system that embodies the features discussed above for a whole country? Consider Nigeria. One can easily document a few striking patterns.

First, policy-making for large projects has been extremely stable – no external shock, no shift in relative prices used to dislodge or reshape projects, once they were agreed. Until recently, almost all large projects were already contained as proposals in a consultant report by Arthur D. Little in 1964. They progressed inexorably, albeit excruciatingly slowly at times. The poster child for the syndrome is the multi-billion dollar Ajaokuta steel plant: conceived in 1964, started in 1979 with outdated Soviet technology, completed in 2004, shut down again and re-opened in 2010. Official commission reports occasionally recommended that several projects be stopped, including Ajoakuta – with no effect<sup>8</sup> . The slow progress may reflect periodic shortage of funds, shift in parts of the economics and also hold-ups by important players in the system. The Bonny LNG project provides a case in point. In 1964 a plane with buyers willing to sign off-take agreements for the gas was on the tarmac in Amsterdam ready to fly to Lagos. Yet, the then petroleum commissioner from the North of the country stopped the process and ordered a review, during which time North Sea oil was discovered. This killed the economics of the project for many years. It was not until 1999 that the plant became operational. The development of large infrastructure projects followed similar patterns. For example, hydro-power plants continued on course, even though gas would have been cheaper (Klein, 1987).

> 8 For example, the Onosode Commission of 1984

9
# **1. Introduction**

With the onset of the global financial and economic crisis in late 2008, concerns were immediately raised over the vulnerability of infants and young children and the likelihood of increases in infant mortality (Friedman and Schady, 2009). Short of increasing mortality, business cycle fluctuations may have considerable impact on early child well-being. Indeed, even in less extraordinary times, it is common for negative aggregate shocks to be associated with increased infant mortality across the developing world, particularly for girls (Baird et al 2007). A growing number of studies establish a link between malnutrition during early childhood and slower physical growth, delayed motor development, lower IQ, and low educational achievement. Improving health and nutritional status, on the other hand, is frequently associated with improvements in longer-term outcomes, including reduced likelihood of chronic disease, increases in educational attainment, and higher subsequent returns in the labor market.<sup>1</sup> The apparent lack of deterioration in child well-being in Indonesia in the wake of the 1997/98 East Asian Financial Crisis stands out as a striking example of the potential capacity for insuring infants and children against the most extreme consequences of adverse economic shocks.<sup>2</sup> Missing from research on the Indonesian case, however, is evidence as to whether policy responses to the crisis can be credited with helping to avert the negative consequences that have been experienced after similar crises elsewhere the developing world. Existing research does not separate the impacts of policy from private family based consumption smoothing efforts, and if policy

> 1See, for example, Alderman et al 2001; Alderman, Behrman and Hoddinot, 2005; Alderman, Hoddinot and Kinsey 2006; Glewwe, Jacoby and King 2001; Glewwe and King 2001; Maluccio et al 2005; Martorell 1999; and Strauss and Thomas, 1998.

> 2Studies using the Indonesian Family Life Survey (IFLS) and rural Central Java survey data have found that there was no significant decline in the health and nutritional status of Indonesia’s children in the wake of the crisis (see Frankenberg et al, 1999; and Strauss et al, 2004; and Block et al, 2004).

2
# **4.1.3 Letters are informative**

In order for reference letters to be effective they must (i) be informative of applicants’ skills and (ii) provide information that cannot easily be inferred from other application documents. We test these assumptions by comparing subjective employer ratings to an objective assessment. Specifically, we regress results from the numeracy and literature aptitude test we administer at baseline on the ratings provided by employers on numeracy and literacy. Table 3 shows that employer ratings and test results are highly correlated for both literacy (col. 1) and numeracy (col. 4). This implies that the average letter contains information about the applicant’s skills.

Next, we explore how the correlation changes when we control for additional covariates. In particular, we control for age, education, gender, and school grades in English and math. The results in Column 2 and 5 show that while coefficients decrease in magnitude, they stay highly significant. This suggests that the letter contains information that employers cannot easily infer from the resume.

Finally, Column 3 and 6 show that the coefficients do not differ by gender. This rules out the possibility that any differential employment effect for women is due to references being more informative of females’ aptitudes.

Overall, Table 3 confirms that referee ratings convey additional information to employers, at least for skills captured in the aptitude test. Arguably, it would be even more difficult for firms to learn about other skills from the CV, especially non-cognitive skills like reliability or work ethics (Aamodt, 2015).

18
|**Score**|<br>**Distance to road (km)**|**Land cover**|**Lithology**|**Slope (degree)**|<br>**Rainfall (mm)**|
|---|---|---|---|---|---|
|2|750-1,000|Forest|Valley<br>Alluvium<br>and<br>Colluvium, Marsh Clay and<br>Peat, Estuarine deposit|<br> <br>5-10|600-700|
|3|500-750|N. A.|N. A.|10-15|700-750|
|4|N. A.|N. A.|N. A.|N. A.|750-800|
|5|N. A.|N. A.|Girujan<br>Clay<br>Formation,<br>Tidal Mud Deposit, Bokabil|<br>N. A.|N. A.|
|6|250-500|Agriculture|N. A.|15-20|800-850|
|7|N. A.|N. A.|N. A.|N. A.|850-900|
|8|N. A.|N. A.|N. A.|N. A.|N. A.|
|9|0-250|Human<br>intervention|Dihing, Dupitila and Tipam|>20|>900|

# **3.2. Landslide and non-landslide locations inventory**

The second step of the proposed approach is to compile an inventory of past landslide locations and develop an inventory of non-landslide locations (i.e., locations where no landslides occurred in the past). Point-based data for past landslides in the CHT and Sylhet divisions were compiled from multiple sources, including Ahmed et al. (2014), Comprehensive Disaster Management Programme II (2012), and GLC. The green points in Fig. 4 show the locations of past landslides in the inventory.

Random and spatially diverse non-landslide points were created in the CHT and Sylhet divisions such that:

- a) The number of non-landslide points is approximately equal to the number of landslide points in each division. A roughly equal number of landslide and non-landslide points were ensured to avoid bias in training the machine learning model, as explained later.

- b) Each non-landslide point is at least 200 m away from the nearest landslide location.

12
44

|Region 3: Northwest|4.14<br>0.044|4.01<br>0.0505|3.03<br>0.0436|5.25<br>0.0335|
|---|---|---|---|---|
||1.28|1.51|1.35|1.31|
|Region 4: Northcentral Coast|‐0.0347|‐0.0254|‐0.0727***|‐0.0504**|
||‐1.56|‐1.04|‐2.89|‐2.41|
|Region 5: Southcentral Coast|0.2475*<br>**|0.2438*<br>**|0.1464***|0.1409***|
||8.97|8.36|4.32|5.55|
|Region 6: Central Highlands|0.0664*<br>*|0.0900*<br>**|0.0411|0.0687***|
||2.08|2.8|1.28|2.75|
|Region 7: Southeast|0.2904*|0.2881*|0.2367***|0.2347***|
||**|**|||
||15.97|13.07|9.76|12.07|
|Region 8: Mekong Delta|0.2757*|0.2701*|0.2518***|0.2157***|
||**|**|||
||11.24|8.42|7.8|9.86|
|1 if year 2004; 0 otherwise|0.3368*|0.3367*|0.3385***|0.33537***|
||**|**|||
||36.4|35.88|35.18|44.92|
|1 if year 2006; 0 otherwise|0.6360*<br>**|0.6387*<br>**|0.6458***|0.64451***|
||69.34|67.54|67.28|88.39|
|**Constant**|7.5505*|7.6402*|7.8854***|7.0465***|
||**|**|||
||95.01|94.78|86.6|67.38|
|r2/ln L|0.57|0.57|0.58|‐23,761.1|
|F|481.45|404.4|359.8||
|N|46985|46985|46985|47,167|

Pooled VHLSS data from 2002, 2004 and 2006. Drought if 20% below annual median rainfall; excess rainfall if > 300 mm in 5 consecutive days at least once in a year; flooding recorded in Darthmouth Flood Observatory and within 2 m elevation difference from river or coast; hurricane force winds if wind 65 knots (118 km/hour) or more.

ln(hours) from 500k city are in effect the ln(1+ tenths of hours) from 500k city with tenths of hours being 6 minute periods
<!-- Start of picture text --> Budget appropriations<br>‘<br>Payroll Electronic file<br>Transactions Of details of su R pment<br>Transactions of checks paid<br>Audit Access with Bank from TSA by Treasury<br>To Treasury<br>for uploading to FMIS<br>Stream1: High Value transactions ‘MIS installed at SU:<br>( above threshold) to FMIS<br>From SU<br>SU communicates with<br>———— el<br>Stream 2:<br>Transactions below<br>the threshold Sent by the SU to the designated Bank via<br>Bank interface or via Credit card/debit card issued by the<br>Bank<br><!-- End of picture text -->
<!-- Start of picture text --> Bangladesh: IPV justification<br>en<br>g<br>a<br>6<br>§ Urban<br>Bo PS<br>E<br>a —TS,/- HS<br>& :<br>Er Higher education<br>2!<br>is)<br>4<br>i)o<br>i<br>1950 1955 1960 1965 1970 1975 1980 1985 1990 1995<br>Year of birth<br>Generated by regressing work status on ipv justification, higher education and interactions<br>with year-of-birth and state fixed effects. Standard errors clustered by state<br><!-- End of picture text --> <!-- Start of picture text --> India: IPV justification<br>EN<br>2°=<br>%<br>8 Urban<br>Bo<br>3 ——— are<br>5 Higher education<br>G<br>ee<br>cc!<br>9<br>4<br>i](a<br>Ti<br>1950 1955 1960 1965 1970 1975 1980 1985 1990 1995<br>Year of birth<br>Generated by regressing work status on ipv justification, higher education and interactions<br>with year-of-birth and state fixed effects. Standard errors clustered by state.<br><!-- End of picture text --> <!-- Start of picture text --> =n Pakistan: IPV justification<br>oa<br>2<br>a<br>>3<br>a<br>5 Urban<br>3§<br>go a ese<br>2<br>CR“<br>g<br>=" Higher education<br>e<br>is)<br>g<br>3)<br>o<br>is<br>1950 1955 1960 1965 1970 1975 1980 1985 1990 1995<br>Year of birth<br>Generated by regressing work status on ipv justification, higher education and interactions<br>with year-of-birth and state fixed effects. Standard errors clustered by state.<br><!-- End of picture text --> <!-- Start of picture text --> Nepal: IPV justificationeg<br>en<br>g<br>‘a5<br>Ae<br>ana<br>&ef Urban<br>€<br>2<br>Bo<br>5<br>Blo ———~—“‘“<br>5 Higher education<br>Er<br>c<br>5<br>a<br>a]<br>O<br>iri<br>1950 1955 1960 1965 1970 1975 1980 1985 1990 1995<br>Year of birth<br>Generated by regressing work status on ipv justification, higher education and interactions<br>with year-oF-birth and state fixed effects. Standard errors clustered by state.<br><!-- End of picture text -->
- 59 -

- dW/dm - (11D5)( (dFw/dnl[dFn/dml - [dF"/dn](dFw/dm] ) - (1/Da)( [2n(a,PZf'(n)-a2<sup>W)v'(W)3[(a,PZf'(n)</sup> + a2W)(v(B)-u(O))J - - 2 [a,PZf"'(n)6s 2(1/a2)(a1PZf'(n)-a2W)v'(W)](-an(v(B)-u(O)I

- - (1/D5){ a1a2PZf''(n)n6S(v(B)-u(O) ) **c** 0.

- dW/dB - (1/D5)( [dFw/dn][dFi/dB] - [dFn/dn]3dFw/dB] _)_

- (l/D.)( [2n(a1PZf'(n)-%aW)v'(W)] ((C1PZf'(n)-a2W)(m-n)v (B) + **6M(-V'** (B))]

- (a1PZf''(n)68<sup>-2(1/a</sup> 2<sup>)(a</sup> 1<sup>PZf'(n)-oW)</sup> 2v'(W)][-a2n(m-n)v'(B)] - (GMD){ -2n(a1PZf'(n)-%aW)v'(W)6Mv'(B) + ,PZf"(n)6Sa2n(m-n)v'(B **)** dW/dB > 0 if 2(a,PZf'(n)-a2W) > (m-n)a1pzf''(n).

- dW/dZ - (1/Do){ (dFw/dn](dF"/dZ3 - CdFn/dn][dFw/dZJ }

- (1/D5){ [2n(a,PZf'(n)-a2<sup>W)v'(W)J</sup> [a,PZf'(n)68 + (a1PZf'(n)-t2<sup>W)nu'(Z)</sup> + a1PZf(n)(v(W)-v(B)+u(Z)-u(O)) + 6uu'(Z)J

- [a,PZf"(n)68<sup>-2(1/a2)(a</sup> 1<sup>PZf'(n)-a</sup> 2<sup>W)V'</sup> v(W)J (-a2nnu'(Z) + a,Pf(n)nv'(W)J )

- - (l/D5){ 2n(a,PZf'(n)-a2<sup>W)v'(W)a,Pf'(n)6</sup> 8 a,PZf"(n)6sa1<sup>PZf(n)nv'(W)</sup> **+** 2n(ca,PZf'(n)-a2<sup>W)v'(W)6Mu'(Z) -a1PZf"(n)6M2nnu'</sup> (Z))

2(a1PZf'(n)-a2W)f'(n) > alf"(n)PZf(n) is a sufficient condition for dW/dZ being positive. If there was no disutility associated by delivering a higher effort u'(Z) would be zero and the stated condition would be a necessary condition.

# **Appendlx** 6t Preliminary empirical results

# Date

The data printed below are from the IMF, International Financial Statistics. The Hungarian data is extracted from the database IMFGES in BESD up to Sept. 1990. The Polish data is from IMFGES and the GUS Bulletin (Statistical Office, Warsaw). The employment index for Poland before 1985 is based on a yearly series from IMF IFS adjusted for seasonal variation using - estimates for the period, 1986(1) 1989(4).
5.26 In the first variation, the existing provincial allocations of resources under the Tariff Compensation Fund are respected. However, all of the resources are devoted to financing the subsistence consumption of those households identified as eligible by the multi-dimensional poverty proxy. The resulting concentration coefficient is –0.22 indicating a reasonably progressive outcome. The errors of inclusion and exclusion come to 41% and 31% respectively. Nevertheless, the fact that the existing pattern of resource allocation does not reflect the geographical distribution of the poor in Argentina, means that the percentage discount that could be financed under this simulation varies dramatically across jurisdictions, ranging from 2% (Buenos Aires Province) to 389% (in some of the smaller provinces such as Rio Negro), with a provincial average of 74%.

5.27 In the second variation, the current provincial allocations of Tariff Compensation Fund resources are adjusted to make them proportional to the number of poor households in each Province (recall Figure 8). This adjustment barely affects the concentration coefficient associated with the simulation, since exactly the same poor households are benefiting in each case. However, it serves to reduce the variation in the percentage discount that can be offered across jurisdictions. Thus, poor households in the Buenos Aires Province would receive a discount of 14%, while those in the smaller provinces of the interior would receive a discount of 11%. At a national level, the average discount across provinces comes to 25%. Therefore, the overall effect of this change is to redistribute resources from the long-standing poor of the interior to the ‘new poor’ in the Metropolitan Area. A more detailed analysis of the effects of this geographical redistribution of resources can be found in Table A6 of the Annex.

**Figure 18: Simulation of alternative social policies for electricity**

<!-- Start of picture text --> 60%<br>50% Existing<br>provincial<br>40% allocations<br>30%<br>20% Modified<br>provincial<br>10%<br>allocations<br>0%<br>1 2 3 4 5<br>Income Quintile<br>Source: OPSM Survey, May 2002<br><!-- End of picture text -->

# **Gas**

5.28 In the case of the gas sector, three alternative uses are considered for the US$100 million collected by the Patagonian gas fund.

5.29 The first of these consists in the introduction of a national social tariff that provides a discount on subsistence consumption of gas to all households that qualify 42