rafmacalaba/data-use-mentions-extended huggingface dataset:
Policy Research Working Paper 8636

# **Abstract**

This paper explores the incentives for backyarding, an expanding category of urban land-use in developing countries that has proliferated South Africa. The theoretical model exposes the trade-off faced by the homeowner in deciding how much backyard land to rent out: loss of yard space consumption in return for a gain in rental income. Under common forms for preferences, the homeowner’s own-consumption of yard space falls as land rent increases, causing more land to be rented to backyarders. With better job access for backyarders raising land rent by increasing their willingness-to-pay, the analysis then predicts that the extent of backyarding will be higher for parcels with good job access. This hypothesis is tested by combining a satellite- based count of backyard dwellings per parcel with job-access data. The empirical results strongly confirm the prediction that better job access increases the extent of backyarding.

This paper is a product of the Development Research Group, Development Economics. 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/research. The authors may be contacted at jkbrueck@uci.edu, clausrabe@hotmail.com and hselod@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
hands tied on explicit migration policy (because of electoral constraints), they can indirectly manage international migration by fine tuning _ad hoc_ visa provisions in RTAs. Second, when _preferential_ migration policies are prevented, governments may target origin-specific foreign labor force inflows through the inclusion of visa provisions in Regional Trade Agreements.

The remainder of the paper is organized as follows: section 2 presents the data constructed for estimations and descriptive evidence, section 3 outlines a theoretical framework underlying our econometric analysis. Section 4 presents the empirical strategy. Section 5 presents the baseline results and some heterogeneity analysis. The last section concludes.

# **2 Data and descriptive evidence**

## **2.1 Data**

Our analysis is based on two main data sources. First, we exploit the data made available by the World Bank Handbook of Deep Trade Agreements (Mattoo et al., 2020) – DTA database hereafter. It provides very detailed information on the content of RTAs and therefore highlights the rising role of deep trade agreements (DTAs) in the process of bilateral trade liberalization of countries. DTAs are defined as trade agreements that cover additional policy areas distinct from those directly affecting trade. For instance, DTAs may include provisions regarding international flows of investment and labor, as well as environmental or intellectual property protection provisions - see (Mattoo et al., 2020) for the list of provisions included in trade agreements. The World Bank data set on Deep Trade Agreements provides information on 18 distinct chapters, each of them authored by lead experts. We focus on Chapter 8: Visa and Asylum which assesses " _the presence, depth, and geographical distribution of visa and asylum provisions in preferential trade agreements (PTAs)_ ". The mapping of ’Visa’ related provisions has been made on the basis of 279 RTAs included in the World Bank’s database.<sup>4</sup> Among them, 100 RTAs include some visa-related provisions.<sup>5</sup> On such a sub-sample of
# **Female Headship and Poverty in the Arab Region: Analysis of Trends and Dynamics Based on a New Typology**

Shireen AlAzzawi, Hai-Anh Dang, Vladimir Hlasny, Kseniya Abanokova, and Jere Behrman<sup>*</sup>

**Keywords** : poverty, feminization, female-headedness typology, synthetic panels, Arab region, household surveys

**JEL Codes** : I3, J16, N35, O1

> * AlAzzawi (salazzawi@scu.edu) is lecturer, Santa Clara University, Santa Clara CA, USA; Dang (hdang@worldbank.org; corresponding author) is senior economist, Living Standards Measurement Unit, Development Data Group, World Bank, USA and is also affiliated with IZA, Indiana University, and London School of Economics and Political Science; Hlasny (vhlasny@gmail.com) is economic affairs officer, UN ESCWA, Beirut, Lebanon; Abanokova (kabanokova@worldbank.org) economist, Living Standards Measurement Unit, Development Data Group, World Bank, USA; Behrman (jbehrman@econ.upenn.edu) is WR Kenan Jr Professor of Economics & Sociology, University of Pennsylvania, USA. We would like to thank Kathleen Beegle, Nazmul Chaudhury, Ebad Ebadi, Daniel Halim, Emily Hannum, Dean Jolliffe, Michael Kevane, Mahdi Majbouri, and participants at the American Economic Association meeting (New Orleans) and the Central Banks of the Middle East and North Africa's Annual Conference on Development Economics (Rabat) for helpful comments on an earlier draft. We would also like to thank the Chief Economist’s Office for the Middle East and North Africa (MNACE) for guidance and financial support and the UK Foreign Commonwealth and Development Office (FCDO) for additional funding assistance through the Data and Evidence for Tackling Extreme Poverty (DEEP) Research Program.
# **7. Appendix: Data**

In this paper we exploit Uruguayan establishment level data covering a considerably long period of time. We use annual establishment level observations from the Manufacturing Survey conducted by the Instituto Nacional de Estadística (INE) for the period 1982-1995. The surveysampling frame encompasses all Uruguayan manufacturing establishments with five or more employees.

The INE divided each four digit International Standard Industrial Classification (ISIC) sector in two groups. All establishments with more than 100 employees were included in the survey; the random sampling process of firms with less than 100 employees satisfies the criterion that the total employment of all the selected establishments must account at least for 60% of the total employment of the sector according to the economic Census (1978 or 1988).

The data for the whole period are actually obtained from two sub sample sets: from 1982 until 1988 and from 1988 until 1995. In 1988 the Second National Economic Census was conducted. After that, the INE made a major methodological revision to the manufacturing survey and changed the sample of establishments. The statistical analysis was also performed controlling for the sample of origin. Firms entering the sample in 1988 behave similarly than firms from the old sample. Differences in behavior in white collar and capital adjustment are small. For the firms in the new sample the slope of the blue collar adjustment function is higher in the creation side while lower in the destruction side. It is hard to evaluate differences by sample in the sign or size of the policy impacts.

In total, we have 627 different establishments present in at least one period. There are 208 starting in 1982, of which just 185 make it to 1995. The 1988 sample, is composed of 304 establishments included for the first time in that year, and 254 from the old sample not all of which are to be followed in subsequent years.

# **7.1. Capital**

To construct the establishment capital stock series, we follow a methodology close to Black and Lynch (1997)
# 1. **Introduction**

Recent work on efficiency wage models has produced an internally consistent macro-theory of involuntary unemployment (underemployment)' whose basic assumptions and implications have largely been corroborated by empirical micro-studies.<sup>2</sup> Although the microeconometric work was motivated by macroeconomic theory, it has never been fully integrated into a macroeconomic framework.<sup>3</sup> In this paper we offer one version of such an integrated approach. More precisely, we first develop an efficiency wage model with labor turnover (Phelps 1968, Stiglitz 1974, Salop 1979, Hoon and Phelps 1992) and show that the worker's decision problem gives rise to a quit-rate function. We then use microeconomic data to estimate this quit-rate function and to test the specification suggested by economic theory. Finally, microeconomic evidence and macroeconomic model are combined to evaluate the quantitative effects of changes in economic policy and other macroeconomic shocks on the wage rate, the turnover rate, and employment in the long-run (steady state analysis).

The efficiency wage model with labor turnover we employ is in principle applicable to any type of movement of labor across sectors in any country (Bulow and Summers 1986). However, the original literature on this type of efficiency wage model has mainly focused attention on unemployment in developed countries (Phelps 1968, Salop 1979, Phelps and Hoon 1992). We, on the other hand, test and calibrate our model using panel data on the movement of Mexican workers between the formal salaried and the informal self-employed sector. Our choice of the data set was motivated by the following two considerations.

'See Katz (1986) and Woodford (1994) for surveys.

2See Katz (1986) and Layard, Nickell, and Jackman (1991) for surveys.

3The quantitative papers by Danthine and Donaldson (1990,1995) and Kimball (1994) on efficiency wage models with shirking (Shapiro and Stiglitz 1984) consider microeconomic evidence when calibrating the macroeconomic model, but do not incorporate a microeconomic
In a study using survey data in Euro area, Gnan et al. (2011) find that education and income are more strongly correlated with inflation expectations than age and gender, and argue that this is consistent with the findings on financial literacy.

# **_In lower-income countries, there is a relationship between financial literacy and having a bank account_**

One of the key questions that arises in developing countries is whether financial literacy and financial access are causally linked. In fact, in most countries surveyed by FinScope, the primary reason cited for not having a bank account is lack of income or the inability to maintain a minimum balance, rather than lack of knowledge. In Malawi, where only 19 percent of the population has a formal bank account, these reasons account for the overwhelming majority of responses. Less than 10 of percent respondents cite financial literacy-related reason, such as not knowing how to apply for an account. (It is possible, however, that perceptions of minimum required balances, for instance, may be incorrect.) At the same time, almost 80 percent had either never heard of savings accounts or did not know what they were, and the figure is lower for current or checking accounts. Income-related reasons are also predominant in Rwanda, Namibia, and Tanzania, although a higher percentage of adults in Tanzania (21 percent) also said that they did not know how to open an account.

In an attempt to provide a more definitive answer, Honohan and King (2009) use data from FinScope surveys to estimate the impact of various potential determinants of having a bank account. They include a variable for financial sector knowledge (a normalized index of the number of products from a given list that a respondent claims to know), which might be regarded as a weak proxy for general financial literacy. They find this variable to be positive and significant when controlling for country fixed effects, but the sign is reversed without the fixed effects. Hence, no clear correlation can be drawn.

Regardless,
assumptions governing return decisions (Ghosn et al. (2021)). Given the importance of information for decisions around return (Alrababa’h et al. (2020)) and gauging living conditions in the home country, leveraging social media sources is of critical relevance because they offer an immediate assessment of returnee preferences and thoughts on return as well as a way to measure how refugees access and consume information that would be pertinent to their return.

**Relevance to the call:** Our paper contributes to work on refugee resettlement and attitudes from other researchers in this call including Bove et al. (2021), Kaplan (2021), and Parry and Aymerich (2021). Bove et al. (2021) notes that refugees are more likely to return to areas with UN peacekeeping missions, as they provide a sense of stability and help with service provision. Local governments and militias in Syria may provide similar services in certain regions (Bove et al. (2021)). Additionally, local peacekeeping agreements, if crafted with the aide of those from all sides, may facilitate safer and lasting return. Such agreements may even work in cases of high tension, such as perceived ISIL affiliation of IDPs, if the IDPs are part of the discussion or trusted leaders work as negotiators for them (Parry and Aymerich (2021)). Finally, strong religious beliefs may increase the likelihood that individuals stay in their home communities even in the presence of violence (Kaplan (2021)), as well as making them more likely to encourage others to return. Our paper augments these studies, which focus on specific factors, by considering return as a whole in terms of discussions occurring in these communities. As a result, we can determine if the factors outlined here are present in social media discourse and to what extent discussion of them differs.

# **3.2 Social Media and Information Access**

Beginning with the protests in 2011, social media has played an integral role in the Syrian
prices to state level regulators.

The paper uses the consumption categories proposed in MOP’s notification to construct the first counterfactual tariff schedule. To illustrate the welfare impacts of this tariff schedule, we assign energy charges from JVVNL’s 2016-17 tariff order to the consumption tiers in the notification. This illustration also uses the consumption tiers of the notification to assign fixed charges instead load categories varying by the sanctioned load. This is because the sanctioned load variable in JVVNL’s data set is either missing or noisily estimated, rendering it unusable for fixed cost imputations at the household level. Since the range of consumption in each tier of MOP’s notification is larger than the range in JVVNL’s tariff order (for instance, the first tier of consumption in JVVNL’s tariff order ends at 50 units, whereas the first tier in MOP’s proposal ends 200 units) for the same energy and fixed charge, one can expect this tariff design to result in higher consumer surpluses across all households but also lead to a revenue shortfall compared to the current tariff.

To study the welfare impacts of this tariff schedule and all other counterfactual schedules thereafter, the following methodology is used. A short-run linear demand curve is calibrated for each household using estimates of electricity demand own-price elasticities in the range of -0.1 to -0.4, which are consistent with the magnitudes estimated from our structural demand model. The calibrated household demand curve passes through the household consumption observed in household level billing data set and the corresponding marginal prices observed in JVVNL’s 2016-17 tariff order. Using this demand curve, consumer surplus at current tariff rates and under counterfactual tariffs can be calculated as the area under the short-run demand curve and above the step-wise marginal price curve, less the applicable fixed charges. The marginal price faced by a household includes the energy charges,

> 19 Willig, R. (1976). Consumer's Surplus Without Apology. The American Economic Review, 66(4), 589-597.

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|
|Post*RCA relative to Poland|0.089**|0.140**|0.033|0.605***|
||(0.036)|(0.057)|(0.043)|(0.211)|
|Post*RCA relative to world|0.124**<br>(0.045)|0.301***<br>(0.078)|0.129<br>(0.081)|0.605***<br>(0.211)|
|N (obs.)|134174|134174|134174|63992|
|N (frms)|18355|18355|18355|13245|

Notes: Authors’ calculations based on survey data from the Central Statistical Office of Poland. Each cell is from a separate regression. All regressions include firm and year fixed effects. TFPR is estimated using the method in Levinsohn and Petrin (2003). RCA is a standard measure of comparative advantages of Germany versus Poland and the world. All measures of German influence refer to the period 1994-2004. Standard errors in parentheses clustered at 2-digit industry level. *10% level, **5% level, and ***1% level.

Table A.9: Placebo test: initial export share for Russia, Ukraine, Lithuania and Poland

||Po|land|R|ussia|Ukr|aine|Lith|uania|
|---|---|---|---|---|---|---|---|---|
|D. Dep. variable: TFPR (ACF)|(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|
|Post _×_ country’s export share (
Figure 4: Share of digital services trade of country-pairs sharing the same data model, by data model by (2015) <!-- Start of picture text --> Cross-border data transfers rules<br>1.5%<br>19.9%<br>78.6%<br>Conditional Open Limited<br><!-- End of picture text --> <!-- Start of picture text --> Domestic processing of data rules<br>4.5%3.8%<br>91.7%<br>Conditional Open Limited<br><!-- End of picture text --> Source: Authors’ calculations using TiVA trade data. Note: Digital services trade covers ISIC Rev. 4 codes 45-82.

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Unfortunately, whereas the second term is easy to estimate from the use of flood maps combined with flood depth – damage curve functions, the first term is impossible to compute without relying on a fully fledged urban economics model as we have described and used throughout this study. It however can be proxied for, through several simplifications. We report below how the changes in central housing rents because of resilience investments can be approximated and provide calculation details in the Appendix. 0 1 1 2 2 + ⋯. (17) 0 RR RR In equation (17), 1 , 2 , ..., represent fractions of the total urban population living in grid cells 1, 2,..., so NNRR ≈1 + bbff MM + bbff MM that = <u>i</u> , where is the population residing in grid cell RR , and is the total population in the urban rr ff ff area. And ii 1 , 2 , ... represent the difference in flood depreciation parameters divided by the initial ii ff NN aa ii NN complete unitary capital costs, so that MM MM = <u>� NR �</u> ~~.~~ ρρffRR−ρρff MMii �ρρffNNRR+ρρ+ii� If we combine (16) and (17) and aggregate over the whole urban area, we can compute the ratio of aggregate land values with and without flood mitigation works, and as: 1 RR NNRR AALLAA AALLAA <u>NR</u> bb (18) II II aa RR ≈�� ffii �aa � AALLAA ρρ+ ii+ ρρ ii ii AALLAANNRR ii=1 ρρ+ ii+ ρρffiiRR �1 + bb�ffii=1 MM With the formula in equation (18) it is possible to proxy for the impacts of flood mitigation investments without developing and calibrating a fully-fledged urban economics model and with only a limited set of data. The data needed to inform equation (18) are the construction function parameter ,
# 2.1. Country level evidence: Which countries experience the most disruptions?

The World Bank’s Enterprise Surveys enable a descriptive analysis of which countries experience the most disruptions to electricity, water, and transport infrastructure. This provides estimates for up to 139 low and middle-income countries, and can be complemented with (partially overlapping) data from the WEF for 96 middle and high-income countries.

Overall, the data portraits a consistent picture: there are substantial differences in the quality of infrastructure across countries. The disruption data from the World Bank’s Enterprise Surveys demonstrate this, and the infrastructure quality index by the World Economic Forum (WEF) confirms it. The quality of infrastructure is generally linked closely to the level of economic development, proxied in Figure 2 by GDP per capita. For transport, water, electricity, and infrastructure more generally, more developed countries tend to have fewer disruptions and higher perceived infrastructure reliability. However, the data also shows that there exist significant differences in infrastructure quality levels for countries at the same income level. For instance, Bhutan’s (GDP p.c. $2,500) reliability of electricity is comparable to many developing countries, while Nigeria ($2,476) ranks among the lowest in the whole sample. Many factors can drive these patterns, and the remainder of this section will discuss and explore these aspects.

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is not particularly high, especially when compared to low-income countries such as Cambodia or Lao PDR.

**Table 6: % of Firms Identifying Labor Skill Level as a Major Constraint**

||All firms|Apparel<br>sector|Exporter|Non-<br>exporter|Small<br>firms|Medium<br>firms|Large<br>firms|Domestic<br>firms|Foreign<br>firms|
|---|---|---|---|---|---|---|---|---|---|
|Cambodia|15.5|-|10.3|15.6|12.8|22.0|18.1|13.4|22.3|
|Indonesia|4.5|4.2|2.4|4.5|4.5|3.7|6.3|4.3|12.9|
|Lao PDR|18.7|**-**|19.1|18.7|10.3|46.9|46.3|16.7|52.4|
|Philippines|7.8|28.7|2.4|8.2|10.0|5.3|5.5|8.4|4.1|
|Vietnam|8.9|11.9|5.5|9.3|5.6|11.1|12.3|7.9|18.3|

_Source: World Bank Enterprise Surveys (Indonesia, Lao PDR, Philippines, Vietnam 2009; Cambodia 2007)_ Looking beyond the national level, it becomes apparent that a significant proportion of foreign firms, as well as medium and large firms, report being constrained by shortage of skills in the labor force. It is important
a positive and significant coefficient on our index of managerial quality in these regressions, which is very stable when employee level controls are included.

Taken together, these results show that while investments by managers in protecting workers are overall low, higher quality managers are better able to protect their workers from pollution exposure.<sup>42</sup> Interestingly, this is not just the consequence of higher quality managers having more financial resources to purchase protective equipment; rather, it also reflects the adoption of different organizational strategies to time production and employee commute such that peaks in road traffic and hence pollution levels, are avoided. This is consistent with higher quality managers being better aware of the negative consequences of pollution on worker productivity.

Finally, it is worth noting that columns 4-9 of the table are obtained from a survey of workers, and do not use, apart from the independent variable (i.e., the index of managerial ability), any information provided by managers. Therefore, our results cannot be contaminated by any reporting bias correlated with managerial ability. Emphasizing the overall result again, 41Employee controls include the employee’s education, age, age squared, cognitive ability (measured through a Raven matrices test), tenure (in years), vocational training (dummy). When explicitly noted, we also control for the employee’s log(salary).

> 42The low overall level of investment in protecting workers from pollution is consistent with the findings of the literature on management practices, which shows that managerial quality is substantially lower in developing countries (Bloom et al. 2013).

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