rafmacalaba/datause-encoder-data huggingface dataset:
- a) Return the farming lands in Xianren village and Shuiweiying village, if within 50 meters to the lake water surface, to wetlands and plant trees and water grasses to purify the water and improve the scenic view of the lake;

- b) Improve the environment of 8 villages which are on the lake banks and 56 nearby villages by building dry latrines for 3935 households, 38 public latrines, 99 garbage collection pools, and some village wastewater and solid waste collection and treatment facilities;

- c) Plant water grasses in the 4 rivers which flow into the lake and build a facility to collect and treat the wastewater at the Yuezheda river.

This project should have significant impacts on the water quality in the lake and rivers and on the overall ecological conditions and natural environment. Even though it is difficult to precisely predict the impacts on the water quality, the preliminary estimations show that the water quality of the surrounding rivers will be improved by one grade level, i.e. from current Grade IV to III. The water quality in the lake will not deteriorate anymore, and part of the water will be improved from current Grade III to II.

# **4. The Survey**

# 4.1 Survey Design and Administration

A contingent valuation survey was conducted in July 2007. Prior to the survey, several focus group discussions were organized, where the project team consulted with various local community groups, including those working on the investment project, government officers, and more importantly the local residents. Perceptions and attitudes toward the local economic and environmental situations, the investment project, the improvement of environmental quality, as well as possible payments for the improvement were discussed. A draft questionnaire was then developed and finalized after three rounds of pre-tests.

In order to better present the project and the outcome, a package of visual aids were developed; examples of the visual aids, which include tables, pictures and maps, are presented in **Appendix 2** . The maps show where and how those projects would be implemented. Pictures and words are used to present the water qualities and the changes. A detailed description of different grades of water quality (Grade I to Grade V or worse, also see **Appendix 3** ) in a plain language and their impacts on daily lives and activities was also developed to describe the water quality changes. A good feature of these WTP surveys is that the 6
# **Poverty in the Brazilian Amazon: An Assessment of Poverty Focused on the State of Pará**

By Dorte Verner<sup>1</sup> World Bank World Bank Policy Research Working Paper 3357, July 2004 _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 view of the World Bank, its Executive Directors, or the countries they represent. Policy Research Working Papers are available online at http://econ.worldbank.org._

> 1 I am very grateful to Lucy Leão and Vilmos Gruvald and their teams from the state government of Pará for assistance on data and other information, Rodolfo Villela Marino for excellent and invaluable research assistance, Lucilene Anderson and Grissel Prieto for office support. The findings, interpretations, and conclusions expressed in this paper are entirely those of the author and should not be associated with the World Bank or its member countries.
Project-Financed PHN Research

-13 -

# Annex. Projects Reviewed

|Region/<br>Country|Project|Fiscal<br>Year|Total<br>Amount<br>of Project<br>(USS million)|Amount<br>of Loan/<br>Credit<br>(US$ million)|
|---|---|---|---|---|
|AFRICA|||||
|Benin|Health Services Development|1989|32.0|8.6|
|Botswana|Family Health|1984|26.6|11.0|
|Burkina Faso|Health Services Development|1985|28.0|26.6|
|Burundi|<br>Population and Health|1988|18.7|14.0|
|Cameroon|<br>Social Dimensions of Adjustment|1990|85.7|21.5|
|Chad|<br>Social Development Action|1990|26.9|13.4|
|Comoros|Health and Population|1984|3.1|2.8|
|C6te d'Ivoire|Health and Demographic|1986|29.7|22.2|
|Ethiopia|<br>Family Health|1988|43.9|33.0|
|Gambia|<br>National Health Development|1987|20.8|_5.6_|
|Gambia|Women in Development|1990|15.1|7.0|
|Ghana<br>|Health and Education Rehabilitation|1986|16.0|15.0|
|Ghana|Second Health and Population|1991|34.3|27.0|
|Guinea|<br>Health Services Development|1988|22.5|19.7|
|Guinea-Bissau|<br>Population, Health and Nutrition|1987|4.4|4.2|
|Kenya|Integratd Rural Health and Family||||
||Planning|1982|61.0|23.0|
|Kenya|Third Population|1988|28.3|12.2|
|<br>Kenya|<br>Fourth Population|1990|41.3|35.0|
|<br>Lesotho|<br>Health and Population|1985|7.4|3.5|
|Lesotho|Second Popation, Health and Nutri-||||
||tion|1990|22.1|12.1|
|Madagascar|Health Sector Improvement|1991|42.5|31.0|
|<br>Malawi|<br>Health|1983|8.7|6.8|
|Malawi|Second Family Health|1987|24.9|11.0|
|Malawi|Population, Healdth and Nutrition Sec-||||
||tor Credit|1991|74.3|_55.5_|
|Mali|Health Development|1984|17.6|16.7|
|Mali|<br>Second Health, Population and Rural||||
||<br>Water Supply|1991|61.4|26.6|
|Mozambique|Health and Nutrition|1989|42.5|27.0|
|Niger|Health|1986|29.3|27.8|
|Nigeria|Sokoto Health|1985|53.0|34.0|
|<br>Nigeria|Imo Health and Population|1989|36.8|27.6|
|<br>Nigeria|<br>Essential Drugs|1990|85.1|68.1|
|<br>Nigeria|<br>Health System Fund|1991|94.5|70.0|
|<br>Nigeria|<br>National Population|1991|93.6|78.5|
|Rwanda|Family Health|1986|14.5|10.8|
|Rwanda|First Population|1991|26.1|19.6|
vestment provisions.<sup>2</sup> Against this backdrop, we make three contributions to the existing literature on the links between deep trade liberalization and FDI. First, we contribute to the debate on whether deep trade agreements with investment provisions stimulate FDI by estimating the direct/partial equilibrium eects of DTAs and DTAs with investment and other provisions on FDI. Second, we use our partial estimates to obtain novel general equilibrium (GE) estimates of the eects of DTAs on FDI. Third, within the same structural framework, we obtain estimates of the eects of DTAs on trade ows, and we translate those eects into general equilibrium eects of DTAs on FDI through trade liberalization.

Guided by the theoretical model of Anderson et al. (2019),<sup>3</sup> we specify two estimating gravity equationsone for trade and one for FDI, which are (i) consistent with and representative of a large number studies that quantify the impact of various determinants on FDI (e.g., Eicher et al., 2012; Blonigen and Piger, 2014; Kox and Rojas-Romagosa, 2020; Laget et al., 2021), and (ii) capitalize on the latest developments in the trade gravity literature (e.g., Head and Mayer, 2014; Yotov et al., 2016). Specically, we rely on the Poisson PseudoMaximum-Likelihood estimator to account for potential heteroskedasticity in the bilateral trade and FDI data and to take advantage of the information in the zero trade and FDI ows (cf. Santos Silva and Tenreyro, 2006, 2011). In addition, we employ a very rich set of xed eects (including origin-time, destination-time, and directional country-pair xed eects), which control for and absorb all possible country-specic and time-invariant bilateral deter-

> 2For example, Lesher and Miroudot (2006) obtain positive eects of investment provisions on FDI, while, more recently, Kox and Rojas-Romagosa (2020) and Laget et al. (2021) do not nd that investment provisions have signicant additional impact on FDI. Moreover, we are not aware of existing work that quanties the full/general equilibrium impact of DTAs and their investment provisions on FDI.

> 3The theoretical model of Anderson et al. (2019) suits our objectives well because it (i) oers structural foundations for both our trade and FDI estimating gravity models and (ii) enables us to translate our partial estimates of the eects of DTAs on trade and welfare into GE eects on FDI. Our contributions in relation to Anderson et al. (2019) are twofold. First, from a methodological perspective, they calibrate the model in a cross section while we build a panel data set to estimate some of the structural equations in order to test for and establish causality in the relationships of interest to us. Second, from a policy perspective, Anderson et al. (2019) simulate a world without FDI, while our aim is to quantify the impact of deep trade agreements on FDI. In policy work that is not intended for publication, Anderson et al. (2016) rely on the framework of Anderson et al. (2019) to quantify the eects of CETA.

3
# **Table 8.2: Determinants of Investment Lending to SMEs (OLS)**

## **Dependent Variable: Share of Investment Loans in Total SME Loans**

Regressions are estimated via OLS at bank level for the year 2007 to 2009. Robust t statistics in brackets and (bank level) clustered t statistics in parenthesis.* significant at 10%; ** significant at 5%; *** significant at 1%

||1|2|3|4|5|6|7|8|9|10|11|
|---|---|---|---|---|---|---|---|---|---|---|---|
|Log Total loans|**-1.49**|**-4.39**|**-4.49**|**-4.13**|**-3.89**|**-0.04**|**-2.70**|**-2.58**|**-2.45**|**-2.30**|**-0.71**|
||[1.82]*|[3.42]***|[3.45]***|[3.25]***|[3.07]***|[0.02]|[2.90]***|[2.67]***|[2.60]***|[2.40]**|[0.66]|
||(1.10)|(2.05)**|(2.06)**|(1.97)*|(1.86)*|(0.01)|(1.78)*|(1.64)|(1.63)|(1.50)|(0.40)|
|GCC Dummy|**-13.96**|**-6.58**|**-3.75**|**-9.01**|**-10.04**|**-14.20**|**-12.94**|**-11.58**|**-15.32**|**-15.60**|**-13.60**|
||[4.50]***|[1.30]|[0.70]|[1.66]*|[1.64]|[2.63]***|[3.11]***|[2.84]***|[3.23]***|[2.94]***|<br>[3.28]***|
||(2.66)***|(0.79)|(0.42)|(1.07)|(0.99)|(1.62)|(1.91)*|(1.75)*|(2.11)**|(1.78)*|(2.04)**|
|State Ownership Dummy|**10.12**|**12.03**|**10.67**|**11.85**|**11.99**|**15.31**|**14.47**|**13.74**|**14.26**|**14.23**|**15.23**|
||[2.36]**|[2.83]***|[2.57]**|[2.81]***|[2.77]***|[3.39]***|[3.22]***|[3.07]***|[3.21]***|[3.14]***|<br>[3.31]***|
||(1.38)|(1.66)*|(1.51)|(1.65)|(1.62)|(2.00)**|(1.88)*|(1.79)*|(1.88)*|(1.83)*|(1.93)*|
|Log Number of Branches||**4.85**|**5.598**|**4.793**|**4.562**|**-0.514**||||||
|||[2.68]***|[2.96]***|[2.66]***|[2.59]**|[0.29]||||||
|||(1.59)|(1.74)*|(1.58)|(1.54)|(0.18)||||||
|Separate Unit for SME Clients Dummy|||||||**6.76**|**8.11**|**6.63**|**5.86**|**3.19**|
||||||||[1.56]|[1.74]*|[1.54]|[1.31]|[0.67]|
||||||||(0.93)|(1.03)|(0.92)|(0.78)|(0.40)|
|Maximum Coverage Registry or Bureau||**0.30**|**0.32**|**0.35**|**0.31**|**0.30**|**0.35**|**0.37**|**0.40**|**0.36**|**0.32**|
|||[1.75]*|[1.91]*|[1.92]*|[1.82]*|[1.84]*|[2.18]**|[2.38]**|[2.34]**|[2.21]**|[2.00]**|
|||(1.20)|(1.32)|(1.36)|(1.25)|(1.32)|(1.48)|(1.60)|(1.64)|(1.50)|(1.44)|
|Legal Rights Index|||**-2.22**<br>[1.59]|||||**-1.85**<br>[1.24]||||
||||(0.95)|||||(0.74)||||
|Time to Register Property||||**-0.04**|||||**-0.05**|||
|||||[1.11]|||||[1.13]|||
|||||(0.82)|||||(0.83)|||
|Time to Enforce a Contract|||||**-0.01**<br>|||||**-0.01**<br>||
||||||[0.98]|||||[0.85]||
||||||(0.58)|||||(0.50)||
|Time to Close a Business||||||**-1.89**|||||**-1.70**|
|||||||[1.20]|||||[1.10]<br>|
|||||||(0.72)|||||(0.66)|
|Partial Credit Guarantee % of GDP||**18.12**|**18.64**|**16.79**|**16.37**|<br>**20.37**|**16.82**|**17.01**|**15.49**|**15.39**|<br>**19.64**|
|||[2.46]**|[2.55]**|[2.26]**|[2.15]**|[2.89]***|[2.17]**|[2.20]**|[1.99]**|[1.96]*|[2.64]***|
|||<br>(1.46)|<br>(1.51)|<br>(1.35)|<br>(1.27)|<br>(1.70)*|<br>(1.29)|<br>(1.30)|<br>(1.19)|<br>(1.17)|<br>(1.55)|
|Observations|238|230|230|230|230|202|227|227|227|227|199|
|R-squared|0.18|0.25|0.26|0.25|0.25|0.25|0.23|0.23|0.23|0.23|0.24|
|Number of Countries|15|15|15|15|15|13|15|15|15|15|13|
|Number of Banks|87|87|87|87|87|77|86|86|86|86|76|
- 50 -

- Economic intllilgence Service. 1987. Agricultural Production In Indla,<sup>State-Wise and</sup> Crop-Wise Data: 1949-50 to 1985-86. Bombay: Centre for Monitoring<sup>the Indian</sup> Economy.

- Economic Intelligence Service. 1987. District Level Data for Key Economic<sup>Indlcators with</sup> 70 Maps. Bombay: Centre for Monitoring the Indlan Economy.

- Economic Intelilgence Service. 19888. Baslc Statistics Relating<sup>to the Ind!an Economy,</sup> Vol.2:States. Bombay: Centre for Monitoring the Indian Economy.

- Economic Monitoring Service. 1986. Basic Statistics on State Economles of India. Bombay: Commerce Research Bureau.

- Flultmen, F. 1983. **The SocloEconomic** **_/Ipact_ of** _Rural Electrificatlon In Developing_ **_Countries: A Review of Evidence._** Geneva: World Employment<sup>Programme Working</sup> Paper, WEP 2-22/WP. 126, ILO.

- Ghosh, A.; Chakravarti, D.; and Sinha, A. 1988. **_Impwct_** of **_Rural_** _Incomes on_<sup>**_theMarket_**</sup> **_for Urban Manufactures._** Economic and Polltical Weekly, October<sup>29:2290-2293.</sup>

- Haggbiade, S. and Hazell, P. 1989. **_Agrlcultural Technology_** _and_ **_Farm-Nonfurm Growth Linkaes._** Agricultural Economics, 3:345-64.

- Hazell, P. and Ramasamy, C. 1986. _Household Expenditure Patterns and_ **_the Growth of the Nonfarm Economy._** Paper presented at the IFPRI/TNAU Workshop on Growth Linkages (Ootacamund, India, February 14-16, 1986).

- Hazell, P.B.R. and Ramasamy, C. 1989. Green _Revolution reconsidered:_ **_The lpwct of_** _the High-Yielding_ **_Rice_** _Varietles In South indla._ (mimeo)<sup>Washington DC:</sup> IFPRI.

- Hazell, P.B.R. and A. Roell. 1983. Rura/ Grcwth LInkagss: Houaold _ExponOittre Patterns In Malaysia ae N.lgrla._ Research Report No. 41, International Food Policy Research Institute, Washington, D.C.

- Harris, S. 1987. _Reglonal Growth Llnkages from Ariculture and Resource Flows In the_ **_Non-Farm_** Economy. Economic and Polltical Weekly 11(1-2):31-46.

- Hossain, M. 1988. **_Nature_** _and_ **_Impact_** _of the Green Revolution In Bangladesh._ Research Report 67, IFPRI, Washington, D.C.

- India. 1961a. Census of Indla 1961. Volume I. Part l-C(I) Subsidiary Tables. New Delhi: Census Commissioner.

- India. 1961b. Census of Indla 1961. Volume I. Part II-A(I) General Population Tables. New Delhi: Census Commissioner.

- India. **1961c.** Census of India 1961. Volume I. Part 1l-A (II) Union Primary Census Abstracts. New Delhi: Census Commissioner.

- Indla. 1981d. Census of indla 1961. Volume I. Part 1l-A (il) General Economic Tables:. New Dolhi: Census Commissioner.
actually did work abroad as overseas foreign workers in 2010.<sup>3</sup> Even allowing for the likelihood that many more people express an interest in migrating abroad than would actually migrate if given the opportunity, these numbers still suggest large interest in migration.

Our qualitative interviews with employment agencies in the Philippines also support the notion of excess supply; it is common to hear reports that the market for overseas contract labor “is a buyer’s market.” In particular, they note that the emergence of Bangladesh, India, Indonesia, Sri Lanka, and Pakistan as competing labor-sending countries has made it more difficult for them to find jobs for Filipinos.

# **2.3 Wage Setting and Minimum Wages**

The Philippine Overseas Employment Administration (POEA) regulates the recruitment and employment of Filipinos for work abroad. Their rules and regulations dictate that there be “guaranteed wages for regular work hours and overtime pay, which shall not be lower than the prescribed minimum wage in the host country or not lower than the appropriate minimum wage standards set forth in a bilateral agreement or international convention, if applicable, or not lower than the minimum wage in the country [the Philippines], whichever is highest.”<sup>4</sup> This rule effectively sets a minimum wage for legal overseas work, since the Philippines Government will not process work contracts which have wages set at a level below that set out in this law.

In practice only some of the host countries for Filipino workers have their own minimum wages that apply to foreign labor. Thus, for example, Filipino workers in the United States, Canada and Korea are covered by minimum wage laws in those countries, whereas other destinations like Saudi Arabia, the United Arab Emirates, Qatar, Bahrain, Oman, and Malaysia do not have minimum wage laws. However, the Philippine Government negotiates bilateral agreements with each destination country, which in some cases set a minimum wage. Work contracts then have to be verified by the Philippine Overseas Labor Offices (POLOs), which, for each occupation, determine the minimum prevailing wage in the host country, and will not approve contracts which set wages below these levels. Thus even more skilled occupations, whose incomes are above the Philippine minimum wage and above the overseas minimum wage

> 3 <u>http://www.census.gov.ph/data/pressrelease/2011/of10tx.html [accessed July 19, 2011].</u>

> 4 <u>http://www.poea.gov.ph/rules/POEA%20Rules.pdf [accessed July 19, 2011].</u>

6
(darker-skinned versus lighter-skinned) and veil (for female candidates), the photos mirrored to the extent possible the actual distribution of both in Tunisia.<sup>12</sup> Finally, fictitious home addresses were constructed following the usual practice: street number, street name, locality, delegation, governorate, and postal code. It was important to ensure that the distribution of governorates in the database of applicants corresponded to the actual distribution of job seekers in Greater Tunis. Therefore, the majority of applicants were assigned Tunis, followed by Ben Arous, Ariana, and Mannouba. The list of delegations, localities, and street names were then selected, relying on Google Maps to construct plausible home addresses. Finally, street numbers were constructed and added.

# Randomization routine

The 1,571 pairs of female and male resumes (3,142 in total) were created by randomly assigning to each the comprehensive set of characteristics devised by the study team and ensuring that, on average, resumes were balanced across genders. The randomization routine was as follows:

- For the applicants’ identities, names (along with the phone number and email address corresponding to each name) and photos were randomly assigned to resumes by gender. Home addresses were also randomly assigned across both male and female resumes (that is, they were cross-randomized).

- Nonproductive characteristics, such as hobbies and club memberships, were randomly assigned across both male and female resumes.

- Productive characteristics, such as certifications and awards, were randomly assigned at the major level across male and female resumes. Other productive characteristics, such as projects, internships, and community life, were randomly assigned at the occupation level across male and female resumes. All inputs were qualitatively similar to one another for the pair of male and female resumes but not identical. This was necessary to avoid the risk of detection.

- The same education degree and institution at the secondary and tertiary level were applied to the pair of male and female resumes at the occupation level. The name of the institution was

> 12 The team relied on local knowledge and consultations to come up with approximate estimates for the distribution of women who wear the veil and the distribution of darker- versus lighter-skinned people in Tunisia.

21
NWSC records amongst the lowest hidden costs when compared to other East African countries (figure 17b).

**Table 9. Evolution of operational indicators associated with NWSC, Uganda, 19992009**

||Water<br>delivered|System<br>losses|Collection ratio|Average total<br>cost|Average<br>effective tariff|Total hidden<br>costs|Total hidden<br>costs|
|---|---|---|---|---|---|---|---|
||(million<br>m<sup>3</sup>/year)|(%)|(%)|(US$/m<sup>3</sup>)|(US$/m<sup>3</sup>)|(US$ million/year)|(% revenues )|
|1999|46|48|76|1.18|0.45|30|243|
|2000|46|43|80|0.93|0.45|22|141|
|2001|47|43|85|0.93|0.45|22|131|
|2002|47|40|92|0.94|0.45|21|113|
|2003|51|39|92|0.88|0.47|20|107|
|2004|55|38|98|0.95|0.50|23|85|
|2005|58|34|89|0.99|0.53|26|88|
|2006|58|30|90|1.01|0.58|25|76|
|2007|61|33|92|1.18|0.81|25|59|
|2008|64|34|92|1.44|0.91|36|66|
|2009|69|36|99|1.22|0.76|32|65|

_Source:_ Derived from Briceño-Garmendia, Smits, and Foster (2009).

**Figure 17. Impressive reduction of hidden costs in Uganda, 1999-2009**

<!-- Start of picture text --> a. Evolution of hidden costs in the NWSC<br>250<br>200<br>150<br>100<br>50<br>0<br>Collection inneficiencies Under-pricing Losses<br>Percentage of the revenues<br><!-- End of picture text -->

- **b. Hidden costs of selected water utilities**

<!-- Start of picture text --> 250<br>200<br>150<br>100<br>50<br>0<br>Tanzania Ethiopia Kenya Sudan Uganda Uganda<br>(2009) (2005)<br>Collection Inefficiencies Underpricing Losses<br>Percentage of the revenues<br><!-- End of picture text --> _Source:_ Derived from Briceño-Garmendia, Smits, and Foster (2009).

30
||_Table 5(Cont’d)_|||
|---|---|---|---|
||**Full Sample**|**Plots[d <= 1km]**|**Plots[d <= 500m]**|
|**_Household Characteristics (Cont’d)_**||||
|Agriculture implement index|0.022***|0.028***|0.030***|
||(0.004)|(0.005)|(0.006)|
|Number of plots in the holding|-0.053***|-0.052***|-0.050***|
||(0.005)|(0.006)|(0.006)|
|Access to non-farm labor income †|-0.054***|-0.052***|-0.065***|
||(0.010)|(0.012)|(0.013)|
|Access to non-Farm non-labor income †|-0.018*|-0.024**|-0.018|
||(0.010)|(0.012)|(0.013)|
|**Observations**|18,256|13,161|9,798|
|**Adjusted R2**|0.430|0.425|0.466|

**Note:** † denotes a dummy variable. *** p<0.01, ** p<0.05, * p<0.1. Constant, district fixed effects (30 in total) included but not reported.

17
**<u>Table 7: Determinants of satisfaction with service delivery</u>**

|||**High sat**<br>|**isfaction with ser**<br>|**vices provided by**<br>||
|---|---|---|---|---|---|
||**Education **|**Health **|**Police**|**Administration **|**Productive services**|
|Predicted knowledge|0.227***|0.083***|0.048***|0.196***|0.171***|
||(8.10)|(2.62)|(3.26)|(6.33)|(8.92)|
|Mean corruption in village|-0.073***|-0.046**|-0.013|-0.011|0.008|
||(4.06)|(2.40)|(1.32)|(0.59)|(0.65)|
|Village expenditure (log)|0.012***|0.008*|0.002|-0.001|0.006*|
||(2.81)|(1.73)|(0.85)|(0.15)|(1.95)|
|Rural|0.005|-0.002|-0.003|0.019***|0.007**|
||(1.06)|(0.37)|(1.08)|(3.47)|(1.99)|
|East Dummy|-0.022***|-0.033***|-0.009***|-0.039***|-0.010**|
||(3.49)|(4.99)|(2.77)|(6.43)|(2.47)|
|North Dummy|-0.032***|-0.034***|-0.010***|-0.041***|-0.016***|
||(4.58)|(4.44)|(2.72)|(5.92)|(3.51)|
|West Dummy|0.021***|0.020***|-0.002|-0.023***|-0.008*|
||(3.30)|(2.86)|(0.71)|(3.63)|(1.88)|
|Observations|11,888|11,888|11,888|11,888|11888|
|Loglikelihood ratio|-2,845.26|-3,161.29|-1,047.38|-3,045.92|-1672.77|

Absolute value of z-statistics in parentheses * significant at 10%; ** significant at 5%; *** significant at 1% 23
emphalone to monitor food access and target interventions.

However, these questions about the FIES raise broader questions about how best to interpret food access metrics in general. Better illustrating the dimensions of poor food access captured by each metric and ensuring a wide range of metrics are collected will help policy makers and researchers best monitor food access and address the mounting challenges. Once provided with this information, then a determination can be made about the weight to give to particular dimensions of poor food access when designing policies and evaluating progress in addressing poor food access. Given the challenging environment that is currently unfolding, with food price inflation accelerating globally (e.g., UN 2022), these issues are becoming increasingly urgent and national statistical systems need to be better prepared for the challenge.

24