rafmacalaba/datause-encoder-data huggingface dataset:
Policy Research Working Paper 10426

# **Abstract**

Measuring poverty entails making interpersonal welfare comparisons, that should account for differences in prices faced by households, both over time and across space. This paper investigates the impact of seemingly minor differences in the practical implementation of price adjustments, by developing an analytical framework that is consistent with standard consumer theory and mindful of the data limitations faced by practitioners. The main result is at odds with common sense: even when multiple price indexes are available, say a food and a nonfood Consumer Price Index, it turns out that using a single price index, the total Consumer Price Index, to adjust the consumption aggregate is recommended. The practice of adjusting the components of the consumption aggregate separately, using matching deflators—food expenditure with the food index and nonfood expenditure with the nonfood index—can lead to a systematic bias in the welfare measure, and consequently in poverty and inequality measures. The direction of the bias can be easily predicted based on the price level and household consumption patterns. On the interplay between spatial and temporal deflation, the findings show that temporal deflation should be carried out before implementing adjustments to spatial cost-of-living differences. The paper illustrates these findings using the Islamic Republic of Iran’s 2019 Household Income and Expenditure survey: the bias in the headcount poverty rate due to incorrect deflation is substantive (5–10 percent for estimates at the national level, 15–20 percent in urban and rural areas, and more than 30 percent for district-level headcount rates). Higher-order Foster-Greer-Thorbecke poverty measures are even more affected.

This paper is a product of the Poverty and Equity Global Practice. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/prwp. The authors may be contacted at sredaelli@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
shares of indigenous producers.

The findings of Aitken and Harrison contrast sharply with the results obtained by Haskel, Pereira, and Slaughter (2007), who find evidence consistent with positive intra-industry FDI spillovers in the United Kingdom. Using a plantlevel panel covering the manufacturing sector from 1973 through 1992, they find that a 10 percentage-point increase in foreign presence in a United Kingdom industry raised the total factor productivity of that industry’s domestic plants by approximately 0.5 percent. They also show that spillover effects were larger for lower-performing plants.

In contrast to Venezuela, foreign affiliates operating in Britain exhibited higher value added per worker relative to indigenous firms in the same industry. They were also responsible for a large share of R&D effort undertaken in the United Kingdom (Griffith, Redding, and Simpson 2004). This finding suggests that foreign affiliates in Britain had the potential to become a source of knowledge spillovers. The sophistication of the British firms and the openness of the country to international trade also suggest that competition externalities were unlikely to be large in the United Kingdom. The observation that lower performers benefited more from spillovers is consistent with the sophistication of the British manufacturing sector and thus the limited room for learning.

The inability of the empirical literature to distinguish between knowledge spillovers and competitive externalities explains why surveys of the literature on 20
Brazil, Thailand, and South Africa adopted such a regime, and several other countries (including those in East Asia that adopted a floating exchange rate following the 1997-98 financial crisis) are currently pondering the feasibility of such an approach.<sup>4 5</sup> Chile's experience is well documented by Landerretche, Morande and Schmidt-Hebbel (1999); accordingly, this section provides a brief discussion of the cases of Brazil, Thailand, and South Africa.

Table 2 summarizes the main characteristics of inflation targeting regimes in Chile, Brazil, South Africa, and Thailand. After the real crisis of January 1999 and the adoption of a floating exchange rate, Brazil adopted on June 21, 1999 an inflation targeting framework for monetary policy (see Bogdanski, Tombini, and Werlang (2000)). The inflation targets as well as the tolerance bands are set by the National Monetary Council, on the basis of a proposal by the Finance Minister. These targets must be set in principle two years in advance. The price index chosen for setting the inflation targets is the Broad Consumer Price Index (IPCA), reported by the National Bureau of Geography and Statistics (IBGE), although since March 2000 monthly measures of core inflation have also been published (see Figueiredo (2000)). The choice of a full index was dictated by the need to mitigate potential credibility problems associated with perceptions of deliberate manipulation of the index.

The operational framework of inflation targeting consists of a set of models to support the monetary policy decision process (including a small-scale structural econometric model, and short-term univariate forecasting models), supplemented with surveys of market expectations of major macroeconomic variables (including inflation and growth). The econometric model focuses on the aggregate demand channel of monetary policy, with a direct effect of changes in nominal interest rates, and an indirect effect through relative prices induced by the effect of changes in the nominal interest rate on the exchange rate.

The targets are considered to be met whenever the observed accumulated inflation during the period January-December of each year (measured on the basis of the variations in the IPCA index) falls within the band. In case the target is missed, the central Bank Governor must issue an open letter to the Finance Minister explaining the causes of the breach, the measures work is only a recent phenomenon; see Schaechter, Stone and Zelmer (1999, p. 12).

4 5In countries like Korea, for instance, the central bank is now required by law to set a price stability target every year in consultation with the government.

45
other sub-sectors and prices are assumed to be constant, an increase in the sub-sector’s share in value added at current prices will result. Second, if increased demand and more efficient production lead to a decline in relative prices, a decline of a sub-sector’s share in value added at current prices will result.

**_Figure 2:  Share of Services Sub-Sectors in Value Added_**

<!-- Start of picture text --> Electricity, Gas & Water Wholesale, Retail Trade & Repair Motor Veh. Hotels & Restaurants<br>Bulgaria Russia Croatia<br>Ukraine Poland EU-15<br>Macedonia Latvia Romania<br>Lithuania Lithuania Slovenia<br>Czech Rep. Slovak Rep. Czech Rep.<br>Slovak Rep. Estonia Bulgaria<br>Poland Macedonia Macedonia<br>Russia Czech Rep. Hungary<br>Estonia Ukraine Lithuania<br>Romania Croatia Estonia<br>Serbia & Mont. EU-15 Latvia<br>Latvia Hungary Poland<br>Hungary Slovenia Slovak Rep.<br>Slovenia Romania Serbia & Mont.<br>Croatia Serbia & Mont. Russia<br>EU-15 Bulgaria Ukraine<br>0% 1% 2% 3% 4% 5% 6% 7% 0% 5% 10% 15% 20% 25% 0% 1% 2% 3% 4%<br>1997-2000 2001-2004 1997-2000 2001-2004 1997-2000 2001-2004<br>Transport, Storage & Communications Financial Intermediation Real Estate, Renting & Business Activities<br>Latvia EU-15 EU-15<br>Ukraine Serbia & Mont. Estonia<br>Bulgaria Slovak Rep. Hungary<br>Lithuania Croatia Bulgaria<br>Estonia Latvia Slovenia<br>Slovak Rep. Slovenia Slovak Rep.<br>Czech Rep. Estonia Czech Rep.<br>Romania Hungary Poland<br>Croatia Macedonia Latvia<br>Russia Russia Romania<br>Macedonia Bulgaria Croatia<br>Serbia & Mont. Czech Rep. Russia<br>Hungary Ukraine Serbia & Mont.<br>EU-15 Romania Lithuania<br>Poland Lithuania Ukraine<br>Slovenia Poland Macedonia<br>0% 5% 10% 15% 20% 0% 1% 2% 3% 4% 5% 6% 7% 0% 5% 10% 15% 20% 25%<br>1997-2000 2001-2004 1997-2000 2001-2004 1997-2000 2001-2004<br><!-- End of picture text --> Source: WIIW database 2005 and KLEMS database 2007.

Notes: For the Baltic countries and for EU-15 countries, the NACE 2-digit sub-sectors in the KLEMS database are aggregated to match the 6 more aggregate sub-sectors in the WIIW database.

In terms of their contribution to value added, the major services sub-sectors across ECA countries between 1997 and 2004are wholesale, retail, and repair of motor vehicles – 13.4% on average – followed by real estate, renting, and business activities – 12.5% on average. Both these sub-sectors gain importance in most ECA countries during this 6
of union coverage and more centralised wage setting, firm-level negotiations tend to have a stronger impact on real wage rigidity, but this result is not uniform across countries.

# **6. Conclusions**

This paper examines the flexibility of wages across European firms. We look at the extent of rigidities in base wages by estimating the frequency of wage freezes (downward nominal wage rigidity) and the incidence of wage indexation (downward real wage rigidity). We address these issues using a unique survey with a large sample of firms and data from fourteen countries. A substantial proportion of firms who participated in the survey report that they have frozen wages or that there exists an automatic link between wages and inflation. Instead, less than 1% of the more than 47 million workers that the survey represents have experienced a wage cut during the five-year period prior to the survey. This leads us to the conclusion that wage rigidities, both nominal and real, are quite prevalent in Europe.

We use multinomial logit regressions to analyse what factors are related to wage rigidity. Our estimations indicate that country effects appear to be significant determinants of downward wage rigidities and that institutional differences between countries are an important factor behind this finding. The regression results imply that high collective bargaining coverage increases real wage rigidity. Analysis of the union contracts negotiated at different levels (firm-level vs higher-level bargaining contracts) implies that firm-level contracts are a more likely source of real wage rigidity in centralised wage-setting environments. However, there is substantial heterogeneity across countries regarding the impact of different types of union contracts. For example, for Belgium we know a priori that 98% of firms are subject to real wage rigidity by our definition (i.e. imply wage indexation) and that this is implemented by sector-level bargaining agreements. For some non-euro area countries (e.g. Poland) outside contracts appear to be more restrictive for wages than firm-level contracts. Another institutional aspect that influences wage rigidity is related to how difficult it is for employers to lay off workers. We find that nominal wage rigidity is positively associated with the extent of permanent contracts. In addition, permanent contracts have a stronger effect on wage rigidity in countries with stricter labour regulations.

Workforce composition also appears to play a significant role in the determination of wage rigidities. Both types of wage rigidity are positively related with the share of high-skilled white collars; downward nominal wage rigidity is positively related with employees’ tenure in the firms under study. Both of these significant relationships are consistent with the 31
8 t P P P Pt t t t-1l where Mt and Pt denote the nominal money stock and the price level in period t. Inflation and expected inflation in period t are definsd as where ret+1 is based on the information set of t. The demand function for real balances in period t is given by L(ret+l). Assuming that. the money market is always in equilibrium and usirg the approximation (l1+)-l-l-r we may rewrite (3) as Let us consider first the discretionary regime. Here reti1 is assumed to be unaffected by a change in monetary growth which affects **ft.** For a fixed real interest rate, ret+, can be regarded as representing the nominal interest rate. In view of this interpretation, the policy maker in the discretionary regime assumes that when he increases Mt. the money market will be equilibrated by a proportional increase in Pt, rather than by a reduction in the interest **-** rate, as can be seen from Mt L(Te,,)Pt.1 We may treat this situ# ...on as one where the nominal interest rate (ret+l) is determined first with Mt being set immediately after that.
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|**DATA INPUT**<br>E0E'j00"Xj:L*L:Z:i'k0i000|XlR|**199;'SX**<br>ECE0-<br>**5/96**|**_19960/7_**|**_'_**197|
|---|---|---|---|
|Current Account Balance (current US$ milL)|-6255|-5029|-5879|
|Interest Payments (current US$ mil.)|4605|4441|5514|
|Exports of Goods and NFS (current US$ mil.)|39657|41607|45109|
|Factor Receipts (current US$ mil.)|1429|1073|1561|
|Workers Remittances (current US$ mil.)|6404|9326|8906|
|Exports of Goods and Services (current US$ mil.)|47490|52006|55576|
|Exports (GNFS) (current LCU mil.)|1258510|1346758|1482446|
|Overall Balance (- Deficit) (current LC1: mil.)|-956802|-1104796|-1322874|
|Interest on External Debt (current LCU mil.)|52182|64818|55462|
|Interest on Domestic Debt (current LCU mil.)|789658|888888|1001800|
|Total Current Revenues (current LCU mil.)|2684936|2957051|3354666|
|GDP at Current Market Prices (LCU mil.)|12179630|14098490|15635520|
|GDP at Constant Market Prices (LCU mil.)|10222850|10964330|11510140|
|GDP at Current Market Prices (US$ mil.)|305504|308848|309760|
|Gross Disbursements (LT+IMF+ST) (US$ mil.)|7140|6682|9044|
|Net Disbursements (LT+IMF+ST) (US$ mil.)|220|37|2493|
|Interest Payments (LT+IMF+ST) (US$ mi.)|4605|4441|5514|
|Debt Outstanding (LT+IMF+ST) (US$ mil.)|94387|93435|94404|
|Index REER X-rate Index (1990=100), IMF|176|177|166|
|Official Capital Grants (US$ mil.)|345|410|379|
|PPG debt/GDP (current $US) (Ext. Debt)|26%|25%|26%|
|Total Govt. Domestic Debt (General Govt.) (curr. LCU mil.)|4962925|5734965|6698931|
|Net Domestic Credit to Govt. (current LC(U mil.)|2577780|2886200|3306190|
|Domestic Debt (current LCU mil.) (Public Finance)|8218824|9154199|10441148|
|Domestic Debt/GDP (current LCU mil.) (Public Finance)|67%|65%|67%|

_Source:_ World Bank Country Data and Author's Calculation
# Ensuring Robust Flood Risk Management in Ho Chi Minh City

Robert Lempert<sup>1</sup> , Nidhi Kalra<sup>1</sup> , Suzanne Peyraud<sup>2</sup> , Zhimin Mao<sup>2</sup> , Sinh Bach Tan<sup>3</sup> , Dean Cira<sup>4</sup> , Alexander Lotsch<sup>4</sup>

**Keywords:** decision-making under uncertainty, long-lived infrastructure investments, climate change, flood risk management, adaptation

**JEL:** C44, D81, H54, O21, Q54

_______________________________________________________________ 1 RAND Corporation, Santa Monica, CA, USA

- 2 SCE, Toulon, France

3 National Institute for Science and Technology Policy and Strategy Studies, Hanoi, Vietnam 4 The World Bank Group, Washington, DC, USA The authors thank Ho Long Phi at the Steering Center for Flood Control in Ho Chi Minh City for his support; the many stakeholders from government, NGOs, and universities in Vietnam for valuable guidance; Jordan Fischbach, Debra Knopman, Keith Crane, Amber Jaycocks, David Groves, and Henry Willis from RAND for advice on model development, project organization, and workshop preparation; Maartje Wise - Hoevenaars at Royal Haskoning for linking our project with her firm’s flood risk management studies; Marianne Fay, Stephane Hallegatte, Dzung Nguyen, Jennifer Sara, Madhu Ragunathan and Abhas Jha from the World Bank the for their support and guidance; Alice Thauvin for collaboration with SCE’s Ho Chi Minh City office; Vu Canh Toan and Nguyen Quynh Anh from the National Institute for Science and Technology Policy and Strategy Studies, Ngo Cong Chinh, Tran Tuan Anh from the Asian Management and Development Institute, and To Quang Toan from the Southern Institute of Water Resource Research; and the peer-reviewers -- Richard Damania, Jordan Schwartz Marcelino Madrigal, Lauren N. Sorkin, Huyen Le, and Igor Linkov -- for constructive comments. This work was sponsored by the World Bank’s Chief Economist for Sustainable Development, Marianne Fay, and co-funded by Korea’s Global Green Growth Institute and the Global Facility for Disaster Reduction and Recovery.
# Appendix Table 2b: Sizes of industries by <u>gender in unorganized sector, 2000</u>

|||Unorg|anized sector|,male|Unorga|nized sector|,female|
|---|---|---|---|---|---|---|---|
|NIC|IndustryDescription|Plants|Empl|Output|Plants|Empl|Output|
|15|Food products and beverages<br>|1,508.4|4,046.5|311,006|205.0|383.1|14,901|
|16|Tobacco products<br>|104.0|391.7|12,408|65.1|88.0|727|
|17|Textiles<br>|481.3|1,531.8|110,196|227.2|373.6|13,277|
|18|Wearing apparel; dressing and dyeing<br>of fur<br>|1,299.7|2,330.6|73,041|702.7|833.2|11,605|
|19|Leather; luggage, handbags, saddlery,<br>harness and footwear<br>|126.4|269.6|18,685|1.7|4.2|501|
|20|Wood and wood products, except<br>furniture; straw and plating<br>|992.8|2,229.6|61,395|216.1|347.1|3,886|
|21|Paper and paper products<br>|37.1|132.5|11,436|20.5|39.3|1,274|
|22|Publishing, printing and reproduction of<br>recorded media<br>|95.2|306.0|23,234|5.6|22.3|1,033|
|23|Coke, refined petroleum and nuclear<br>fuel<br>|5.4|17.6|2,468|0.1|0.3|11|
|24|Chemicals and chemical products<br>|27.2|127.9|16,866|5.1|13.8|1,058|
|25|Rubber and plastic products<br>|33.2|133.1|18,986|9.1|19.3|2,640|
|26|Other non-metallic mineral products<br>|581.0|2,107.4|77,685|25.3|73.3|4,310|
|27|Basic metals<br>|13.3|51.9|8,266|0.3|1.7|347|
|28|Fabricated metal products, except<br>machinery and equipments<br>|261.6|690.7|60,353|5.4|24.1|2,747|
|29|Machinery and equipment, n.e.c.<br>|45.3|176.8|29,657|1.3|6.4|1,118|
|30|Office, accounting and computing<br>machinery<br>|0.1|0.6|115|-|-|-|
|31|Electrical machinery and apparatus,<br>n.e.c.<br>|30.9|142.9|276,843|0.9|4.3|401|
|32|Radio, television, and communication<br>equipment and apparatus<br>|2.8|14.2|2,075|0.1|1.0|155|
|33|Medical, precision and optical<br>instruments, watches and clocks<br>|3.7|12.4|1,959|0.1|0.4|47|
|34|Motor vehicles, trailers and semi-<br>trailers<br>|7.3|28.7|5,156|0.2|1.0|137|
|35|Other transport equipment<br>|7.4|31.4|5,801|0.1|0.3|91|
|36|Furniture, manufacturing n.e.c.<br>|371.6|937.3|76,912|55.3|92.2|3,724|
||Traditional|1,529.7|2,280.4|57,984|5,859.1|14,973.7|836,350|
||Modern|17.3|48.5|6,006|176.6|737.5|368,193|

Notes: See Appendix Table 2a.
## **5.6 Cost-Benefit Analysis**

Cost estimates provided by Glasswing International indicate that the average cost of a seven-month-long ASP intervention per student is US$296.5. The cost is US$269.4 for _Clubs_ , US$292.5 for _Mindful_ , and US$327.6 for _Virtue_ .<sup>53</sup> Based on the literature that estimates the between human interventions and on adult earn- relationship capital impacts ings (Holla et al., 2021; Ganimian et al., 2021; Galasso and Wagstaff, 2019) and following the framework of Hendren and Sprung-Keyser (2020) for estimating the marginal value of investments using public funds, we conducted a calculation of the program’s approximate benefit–cost ratio.<sup>54</sup> Our estimates indicate that the present discounted value of earning gains expected to result from ASP-induced improvements in behavior at school and an indirect potential reduction in school dropout yields a benefit–cost ratio that ranges from 11.3 to 45.2. Consequently, this program should be encouraged as a public policy, as the intervention is likely to pay for itself even in the short run and has the potential to generate large additional welfare gains in the long run.

# **6 Conclusions**

This paper provides an experimental evaluation to understand the effect of different ASP curricula implemented in three developing and highly violent countries on adolescent behavior at school, academic performance, socio-emotional skills, and emotion regulation. To our knowledge, this is the first study to use AI technology and task-based games to estimate proxies for the difficult-to-measure competencies of socio-emotional skills and emotion regulation.

To achieve our end, we created exogenous variation by randomly assigning partici-

> 53Similar interventions involving at-risk youths in the USA (Heller et al., 2017) are 6.8 times more expensive per participant (in nominal terms, or 3.1 times more expensive in PPP terms) than the average of the interventions involved in this study.

> 54For more details of our approach, see Appendix 4 and Table A12.

35
excludes the considerable assets of South African mutual funds/unit trusts, which have been growing rapidly over the past several years.<sup>9</sup>

**Table 2.2 Assets of financial intermediaries as a percentage of GDP in focus countries and comparators**

As <u>of end-2006 or most recently available</u>

|Country|Deposit money<br>bank assets as %<br>of GDP|Total pension<br>assets<br>(US$ millions)|Total pension<br>assets as % GDP|Total insurance<br>assets (US$ millions)|Total insurance<br>assets as %<br>GDP|Total assets of<br>financial<br>intermediaries as %<br>GDP|
|---|---|---|---|---|---|---|
|Benin|17.0|125.0|3.1|71.9|1.5|21.6|
|Burkina Faso|20.0|—|—|53.0|1.0|21.0|
|Cameroon|11.6|—|—|252.4|1.5|12.9|
|Cape Verde|86.7|170.0|18.5|18.9|2.1|107.3|
|Chad|5.5|n.a.|n.a.|9.9|0.4|5.9|
|Congo, Dem.<br>Rep.|2.6|n.a.|n.a.|18.8|0.3|3.0|
|Côte d’Ivoire|18.5|—|—|491.2|3.0|21.4|
|Ethiopia|12.6|—|—|171.9|1.5|14.2|
|Ghana|34.9|1,076.7|10.9|96.9|1.1|46.9|
|Kenya|36.0|1,770.3|9.4|1,308.7|7.0|52.4|
|Lesotho|12.8|—|—|250.5|13.9|26.7|
|Madagascar|13.2|62.4|1.2|89.7|1.8|16.2|
|Malawi|13.3|n.a.|—|221.6|10.0|23.3|
|Mozambique|18.6|—|—|198.4|2.9|21.5|
|Namibia|68.3|3,312.0|58.0|2,240.0|39.2|165.5|
|Niger|10.0|78.7|2.2|22.7|0.7|10.7|
|Nigeria|20.6|3,507.8|2.9|1,575.2|1.6|25.7|
|Rwanda|16.0|132.7|7.4|27.7|1.7|25.1|
|Senegal|30.3|—|—|149.9|1.9|32.2|
|South Africa|83.1|80,202.3|37.0|173,913.0|72.0|192.1|
|Sudan|16.8|—|—|96.1|0.6|17.0|
|Tanzania|17.6|577.9|4.4|123.5|1.0|22.9|
|Uganda|17.3|470.6|5.2|70.4|1.0|23.5|
|Zambia|13.6|314.1|3.0|121.1|1.2|17.8|
|Chile|71.7|88,293.5|65.0|25,542.6|18.8|155.5|
|Malaysia|125.2|69,659.0|53.2|30,715.9|20.6|199.1|

Sources: Pension system and insurance sector asset data sourced from national pension funds and financial authorities; CEMA for the five WAEMU countries and Cameroon; Axco country reports. _Note_ : Total pension and insurance sector assets are underestimated for several countries. — = Not available; n.a. = Not applicable.

Although three of the African focus countries have relatively high ratios of total assets of financial intermediaries to GDP (Cape Verde, at 107.3 percent, as well as South Africa and Namibia), the next 9 As of end-June 2006, South Africa’s 678 mutual funds (unit trusts) managed an estimated $62.76 billion in assets, up from $52 billion in assets managed by 567 funds one year earlier (EIU 2006a, which cites the Unit Trusts Survey).

10