rafmacalaba/data-use-mentions-extended huggingface dataset:
sectors (Swinnen 2011). Survey-based evidence also suggested no net deterioration in selfreported food insecurity (Headey 2013; Verpoorten et al. 2013) or poverty (World Bank 2014) during the food price hikes of 2007–2008. Moreover, recent general equilibrium models for India (Jacoby 2013), Uganda (Van Campenhout et al. 2013) and a larger 31-country sample (Ivanic and Martin 2014) all find that higher agricultural prices reduce poverty in the longer run through a combination of agricultural supply and wage responses.

In this paper, we aim to further explore this question by examining the relationship between changes in food prices, agricultural production. and poverty across a large swathe of developing countries. While there is a large and related literature statistically analyzing the drivers of poverty reduction using cross-country data (Christiaensen et al. 2011; De Janvry and Sadoulet 2010; Loayza and Raddatz 2010; Ravallion et al. 2007), and within that literature a handful of papers exploring the links between inflation and poverty reduction (Easterly and Fischer 2000; Ravallion and Datt 2002), the present paper appears to be the first cross-country empirical examination of whether higher food prices help or hinder net poverty reduction. A more empirical examination of this question is important given that simulation models are methodologically somewhat opaque because of their complexity, potentially sensitive to the assumptions and parameters that feed into the model, and silent on how quickly agricultural production and wages might respond to higher prices.<sup>2</sup> Hence, the empirical analysis in the present paper overcomes these deficiencies to some extent and should therefore be seen as a complement to the more structural insights afforded by simulation approaches.

The remainder of this paper is structured as follows. Section II briefly reviews existing theory and evidence on the impact of food prices on poverty and welfare. Section III discusses data and methods. Section IV tests the relationship between changes in food prices and changes in prices (both domestic and international). Section V explores likely explanations of this result,
- Pogge, T. and Wisor, S. (2016). ‘Measuring Poverty: A Proposal’, In: M. Adler and M. Fleurbaey (eds.), _The Oxford Handbook of Well-Being and Public Policy_ . Oxford: Oxford University Press.

- Roelen, K. (2018). ‘Poor children in rich households and vice versa: A blurred picture or hidden realities?’, _European Journal of Development Research_ , 30, pp. 320–341.

- Roelen, K., Gassmann, F., and de Neubourg, C. (2010). ‘Child poverty in Vietnam: Providing insights using a country-specific and multidimensional model’, _Social Indicators Research_ , 98(1), pp. 129–145.

- Rogan, M. (2016). Gender and Multidimensional Poverty in South Africa: Applying the Global Multidimensional Poverty Index (MPI). _Social Indicators Research,_ 126: p.987-1006.

- Scott, J., Crompton, R. and Lyonnette C. (eds) (2010). _Gender Inequalities in the 21st Century_ . Cheltenham: Edward Elgar.

Sen, A. (2009). _The Idea of Justice_ . Cambridge, Mass.: Belknap.

- South African Human Rights Commission (SAHRC) and UNICEF. (2014). _Poverty Traps and Social Exclusion among Children in South Africa 2014_ . Pretoria: SAHRC and UNICEF.

- Trani, J.-F. and Cannings, T. I. (2013). ‘Child poverty in an emergency and conflict context: A multidimensional profile and an identification of the poorest children in Western Darfur’, _World Development_ , 48(C), pp. 48–70.

- UNDP. (2014). _UNDP’s Multidimensional Poverty Index: 2014 Specifications_ . New York: Human Development Report Office.

- UNHCR. (2020). Ethiopia country refugee response. Retrieved from https://reliefweb.int/report/ethiopia/ethiopia-country-refugee-response-plan2020-2021.

- Ura,
the UEUS/LFS for those who are engaged in self-employment jobs. The monthly wage is overall higher among public employees than private employees. In addition, monthly wages are higher in larger towns, as the average wages are ETB 762 in small towns and ETB 1,292 in Addis Ababa. We also calculate hourly wages by dividing earnings by the number of hours worked.

# **4 Empirical Strategy**

Our primary econometric approach is pooled ordinary least squares (OLS) and 2SLS at the individual level. The baseline pooled OLS model for individual _i_ in town _j_ at year _t_ (either 2012 or 2013) is where _yijt_ , is a binary indicator of a labor outcome for individual _i_ (for example, one if self-employed and zero otherwise), _Popjt_ indicates the log population of town _j_ at year _t_ , and _β_ 1 is the parameter of interest indicating the probability change in the outcome with respect to the change in a log town population. _Xijt_ is a vector of individual or household characteristics for control.

We control for a set of individual and household characteristics. Those include individual’s age, sex, marital status, and household size. Their educational attainment is controlled by the following categories: no education, primary incomplete, primary complete, secondary incomplete, secondary complete, higher education, and adult education. The summary statistics are reported in Appendix B.

_γt_ is a year fixed e↵ects to deal with year-specific e↵ects since the UEUS and the LFS data were collected in di↵erent years. The standard error is clustered by the town level.

We rely on IVs to reduce an endogeneity bias. There are three sources of possible endogeneity problems in our models. The first stems from the correlation of unobserved town characteristics with town population and labor outcomes. Another problem is reverse causality, that is, workers move to towns where labor outcomes would be better. These two problems are econometrically equivalent. The third problem is about how to distinguish location e↵ects from the sorting
I POLICY RESEARCH WORKING PAPER 2322

# Summary findings

The Egyptian food subsidy system is an untargeted system that is essentially open to all Egyptians. For this reason, the budgetary costs of this system have been high and the ability of this system to improve the welfare status of the poor has been questioned.

Since the food riots of 1977, Egyptian policymakers have been reluctant to make large changes in their food subsidy system. Rather, their strategy has been to reduce the costs and coverage of this system gradually. For example, since 1980 policymakers have reduced the number of subsidized foods from 20 to just four.

Despite these cutbacks, Adams uses new 1997 household survey data to show that the Egyptian food subsidy system is self-targeted to the poor, because it subsidizes "inferior" goods. In urban Egypt, for instance, the main subsidized food - coarse baladi bread - is consumed more by the poor (the lowest quintile group of the population) than by the rich (the highest quintile). So subsidizing baladi bread is a good way of improving the welfare status of the urban poor. But in rural Egypt where the poor do not consume so much baladi bread, the poor receive less in income transfers than the rich.

In many countries, administrative targeting of food subsidies can do a better job of targeting the poor than self-targeting systems. In Jamaica, for example, poor people get food stamps at health clinics, so the Jamaican poor receive double the income transfers from food subsidies that the Egyptian poor receive. But starting a comparable system in Egypt would be costly both in financial and political terms, because many nonpoor households currently receiving food subsidies would have to be excluded.

For these reasons, it is likely that the government will continue to refine the present food subsidy system, perhaps by eliminating current subsidies on sugar or edible oil. Neither of these foods is an "inferior" good, so eliminating these subsidies will have only a minimal impact on the welfare status of the poor.

- This paper a product
Alkire, S. (2018). ‘The research agenda on multidimensional poverty measurement: important and as-yet unanswered questions’, _OPHI Working Paper 119_ , University of Oxford.

- Alkire, S. and Apablaza, M. (2016). “Multidimensional poverty in Europe 2006–2012: Illustrating a methodology.” _OPHI Working Paper_ 74, University of Oxford.

- Alkire, S. and Santos, M. E. (2014). ‘Measuring acute poverty in the developing world: Robustness and scope of the multidimensional poverty index,’ _World Development_ , 59, 251-274.

- Alkire, S., Apablaza, M., and Jung, E. (2014). ‘Multidimensional Poverty Measurement for EU-SILC Countries.’ _OPHI Research in Progress_ 36c, University of Oxford.

Alkire, S. and Foster, J. (2011). ‘Counting and multidimensional poverty measurement’, _Journal of Public Economics_ , vol. 95(7–8), pp. 476–487.

- Alkire, S., Foster, J. E., Seth, S., Santos, M. E., Roche, J. M., and Ballon, P. (2015). _Multidimensional Poverty Measurement and Analysis_ . Oxford: Oxford University Press.

- Alkire, S., Meinzen-Dick, R. S., Peterman, A., Quisumbing, A., Seymour, G., and Vaz, A. (2012). The Women’s Empowerment in Agriculture Index. _IPRI Discussion Paper_ 01240. International Food Policy Research Institute.

Alkire, S., Ul Haq, R. and Alim, A. (2019). ‘The state of multidimensional child poverty in South Asia: a contextual and gendered view’, OPHI Working Paper 127, University of Oxford.

Batana, Y. (2008). ‘Multidimensional Measurement of Poverty in Sub-Saharan Africa’. (2013) _Social Indicators Research,_ 112(2), 337-362.

Bessell, S
<!-- Start of picture text --> Figure 20. Cumulative change in weighted average<br>log labor productivity, 2014-2021<br>(transportation and storage, constant 2010 prices)<br>0.2<br>0<br>-0.2<br>-0.4<br>-0.6<br>-0.8<br>-1<br>-1.2<br>-1.4<br>2014 2015 2016 2017 2018 2019 2020 2021<br>Within-firm Between-firm Entry* Exit* Total<br>Log-changes<br><!-- End of picture text --> Note: Averages are weighted by firms’ share of labor. Source: World Bank and ZRA staff calculations based on CIT database <!-- Start of picture text --> Figure 21. Cumulative change in weighted average<br>log labor productivity, 2014-2021<br>(financial services, constant 2010 prices)<br>0.6<br>0.4<br>0.2<br>0<br>-0.2<br>-0.4<br>-0.6<br>-0.8<br>2014 2015 2016 2017 2018 2019 2020 2021<br>Within-firm Between-firm Entry* Exit* Total<br>Note: Averages are weighted by firms’ share of labor.<br>Source: World Bank and ZRA staff calculations based on CIT<br>database<br>Log-changes<br><!-- End of picture text --> The most notable exception to this overall trend is the accommodation and food service subsector, which has experienced overall productivity growth over the 2014-2021. Further study is needed to assess the drivers of the sector’s strong labor productivity performance, but the broad trend may be related to recent high-profile foreign direct investment (FDI) in the sector, including
ownership measures. The standard ownership measure used in the literature is constructed as the number of shares held by certain investors (e.g. domestic or foreign investors) divided by the total number of outstanding shares at a given point in time. As described in Section II, Worldscope reports the number of outstanding shares at year-end. However, our database on holdings is not as consistent – portfolio holdings are reported on different dates. Therefore, we are not able to construct confidently an ownership measure given that numerator and denominator would not be consistently measured at the same point in time. Hence, we focus on two other variables. First, we analyze a dummy variable capturing mutual fund ownership – it equals one when a firm is held by a certain mutual fund type and zero otherwise. In this case, maximum-likelihood logit models are estimated.<sup>22</sup> Alternatively, we use count data: the number of funds with a certain mandate that holds assets of a given firm in a certain year. The count variables however contain evidence of over-dispersion, and thus negative binomial maximumlikelihood models are estimated.<sup>23</sup> Both variables capture the relevant information for the purposes of the analysis conducted here. We are able to quantify the impact of firm-level attributes on the portfolio choice of mutual funds, although we are not able to measure their impact on the size of mutual fund investments. Multivariate models dividing mutual funds according to their mandate, namely global and specialized funds, are estimated. The multivariate setup accounts for any residual correlation between the equations for different investor types. We are thus able to perform hypothesis tests on the estimated coefficients across equations.

> 22 We do not report estimates based on a linear probability models as it predicts probabilities outside the 0-1 range for a significant number of observations in our sample.

> 23 A poisson model assumes that the mean and the variance of the count data are equal. However, in our dataset, the variance is considerably larger, making
and Customs Union. The acronym DDS designates 'Data Dissemination System'.

Tariff information is provided via <u>Taric code, and Taric description. Information on quotas in</u> force <u>via origin or order number.</u> Information on autonomous tariff quotas coming under Council Regulation (EC) No 2505/96, in preparation, via <u>publication cycle or chapter.</u> The EUROPA webserver provides Customs Office Information (Perform Queries on COL <u>Information) and Customs Office Data (Download the data).</u> Other database includes VIES VAT number validation, AUTONOMOUS TARIFF SUSPENSIONS, European Binding Tariff Information (EBTI), and MRN Follow-up Information (international movements only).

22 __________________________________________________________________________________________________________
*|0.11|-0.01|-0.11|-0.00|
||[-1.02]|[2.82]|[1.57]|[-0.65]|[-1.39]|[-0.10]|
|Constant|0.09***|0.14***|0.12***|0.08***|0.07***|0.08***|
||[10.55]|[10.40]|[10.91]|[24.29]|[4.16]|[17.43]|
|Obs.|798|798|798|798|798|798|
|R-squared|0.18|0.61|0.35|0.56|0.10|0.48|

Notes: Robust t-statistics in brackets. *** p<0.01, ** p<0.05, * p<0.1. All regressions use province fixed effects and report t-stats consistent with robust standard errors clustered by province. All regressions analyze 57 urban provinces. A province is defined to be urban if it contains any part of an urban agglomeration. Panel C uses data exclusively from urban districts in a province, which are defined to be districts that contain any part of an urban agglomeration. Panel D uses data from grid cells that were categorized by the GHLS in 2000 to be urban.

24
# **Figure 7 Exchange rate regimes and capital account openness**

# **A. Exchange rate regime flexibility in EMDEs B. Capital account openness**

<!-- Start of picture text --> Index<br>Commodity importers<br>1.0<br>Percent Hard peg Soft peg Floating Commodity exporters<br>100 0.8<br>80 0.6<br>60<br>0.4<br>40<br>0.2<br>20<br>0.0<br>0<br>Commodity exporters Commodity importers<br>1971 1974 1977 1980 1983 1986 1989 1992 1995 1998 2001 2004 2007 2010 2013 2016 2019<br>1990 2019 1990 2019<br><!-- End of picture text --> _Sources_ : Chinn and Ito (2006); Ilzetzki, Reinhart, and Rogoff (2022); World Bank.

A. Panel shows the share of EMDEs with hard peg, soft peg, and floating exchange rate regimes, based on the classification in Ilzetzki, Reinhart, and Rogoff (2022).

B. Based on Chinn-Ito index of financial openness. The lines represent the average of the index in the represented year. Sample includes 98 commodity importers and 98 commodity exporters.

36
our sample the bulk of the funding source consists of deposits, with a share of 0.771.

We consider two macroeconomic controls from the World Development Indicators (WDI, 2011) database. These are GDP per capita in thousands of constant 2000 dollars, and inflation measured as the percentage change in the GDP deflator. Finally, the bank crisis variable is a dummy variable signaling a country is experiencing a banking crisis (Laeven and Valencia, 2010).

# **3. Methodology and empirical results**

In section 3.1, we describe the system GMM estimation that we apply to our empirical specifications. In section 3.2, we present our results regarding the relative procylicality of state 7
# **1. Introduction**

Urban planning requires ex-ante assessments of potential policy impacts. These impacts need to be assessed at the scale of a whole city, with the understanding that a city forms a system and that policies can have systemic consequences. For instance, urban planners may try to anticipate how land markets could respond to a major transport infrastructure investment, potentially modifying the spatial organization of a city and its footprint over the long term. Doing so requires an understanding of the market forces that drive city structure, including housing construction decisions, household location and transport mode choice, subject to physical constraints and zoning. To answer such questions, urban economists have come up with sophisticated spatial simulation tools which are grounded in urban economic theory and which can be used to support urban planning at the local government level. Land use and transport integrated (LUTI) simulation models which simultaneously predict land use and transport decisions have been designed for a few major cities in the world, and various modeling approaches have emerged, including TRANUS (de la Barra, 1989), UrbanSim (Wadell, 2000) and RELU-TRAN (Anas and Liu, 2007) among others (see Acheampong and Silva, 2015, for a full literature review of such models). Although these simulation models have mostly been applied to cities in the United States and Europe, they are increasingly being applied to metropolitan areas elsewhere (see for instance the recent applications of the RELU-TRAN model to Beijing (Anas and Timilsina, 2015) or Cairo (Anas et al., 2017)). In the case of South Africa, a local version of UrbanSim was developed by the Centre for Scientific and Industrial Research (CSIR) for the East Rand area, Durban and Nelson Mandela Bay (see Wray and Cheruiyot, 2015, for a survey of land use modeling in South Africa).<sup>1</sup> Although the development of LUTI models for developing countries is facilitated by improved data availability, it also faces at least three major challenges
characteristics. The year effects control for time-varying factors that affect all countries, such as changes in reporting in the DFO archive.<sup>2</sup> We also include in **Z** two proxies for land-use patterns: the percentage of urban population and total forest area. Increased urbanization and the related increase in impervious surfaces are believed to exacerbate flooding. Despite widespread belief that forests can prevent and reduce floods, the effect of forests on the probability and magnitude of flood events remains controversial (FAO and CIFOR 2005). Bradshaw et al. (2007) used global flood data to show that forests are associated with lower flood risk and severity. Using the same data, Van Dijk et al. (2009) offered an alternative explanation: floods are correlated with population exposure, which Bradshaw et al. omitted. We therefore control for both forest area and population.

Equation (1) is our benchmark specification, but we consider variants with additional controls. We add domestic credit to the private sector to capture access to private investments in self-protection against flooding. We include a Gini coefficient to capture the degree of inequality in the distribution of income within a country, which Kahn (2005) and Anbarci et al. (2005) found to be an important factor explaining earthquake fatalities. Gini values range from zero to one, with values closer to one indicating greater inequality. Finally, as in Kellenberg and Mobarak (2008), we allow for potential nonlinear effects of income through a quadratic specification. We also allow for nonlinear effects of governance, which resource economics studies outside the realm of natural disasters have found to be significant (e.g., Ferreira and Vincent 2010).

> 2 An alternative approach for addressing potential changes in data quality is to limit the sample to only recent years, which Wheeler (2010) does in a study based on EM-DAT data. This is a less desirable approach in our analysis, given that we rely on within-country variation to identify the effects of
inequality based on the 1998 benchmark data, Milanovic (2005, p. 104) used 63 consumption instruments and 59 income instruments. This represents a significant increase in the number of consumption instruments compared with ten years before (80 income-based and 22 consumption-based distributions), but for some of the most important counties (like China) one still depends on income data alone. If a guess had to be ventured, it could be said that there is likely to be a tendency toward greater use of household per capita consumption as the welfare indicator. This, while quite defensible from a strictly methodological perspective, will open up a number of issues of comparability since most historical income distribution statistics (e.g. in the United States, UK, France, Germany) are income-based. One thus needs to balance methodological and quality improvements in the snapshots of today’s income distribution against the break in the historically existing series.

After the problems of what is the proper welfare indicator, it would seem that other methodological issues should be easier to deal with. This is true. Whatever “income” is, that “income” should be expressed in per capita terms and should be equal across members of a household. This means that the two issues that are often debated in national inequality studies are “solved” here: the issue of equivalence scales, and intrahousehold inequality. These are “solved” because at the current level of statistical development, there is simply no way to account for economies of scale and size across different countries. The main reason for this is that economies of scale and size depend on relative prices of public and private goods (if housing is very cheap, economies of size will be small) and they systematically differ between poor and rich countries.<sup>20</sup> Until we have a better handle on the relative prices of public and private goods,<sup>21</sup> we cannot adjust internationally for equivalent units. A use of a given equivalence scale