rafmacalaba/datause-encoder-data huggingface dataset:
# **Updating Poverty Estimates at Frequent Intervals in the Absence of Consumption Data: Methods and Illustration with Reference to a Middle-Income Country**

Hai-Anh H. Dang, Peter F. Lanjouw, and Umar Serajuddin<sup>*</sup> Key words: poverty, imputation, consumption, household survey, labor force survey, Jordan JEL codes: C15, I32, O15

> * Dang (hdang@worldbank.org) and Lanjouw (planjouw@worldbank.org) are respectively Economist and Research Manager at the Poverty and Inequality Unit, Development Research Group; Serajuddin (userajuddin@worldbank.org) is Senior Economist at the Development Data Group; all three are at the World Bank. We would like to thank Eric le Borgne, Jose Antonio Cuesta, Kristen Himelein, Dean Jolliffe, Nora Lustig, Yusuf Mansur, David Newhouse, Dominique van de Walle, Paolo Verme, and participants at the 17<sup>th</sup> World Congress of the International Economic Association (Dead Sea, Jordan) for helpful discussions on earlier versions. We are thankful to Orouba Al-Sabbagh, Mukhallad Omari and Zein Soufan at the Ministry of International Planning and International Cooperation, and to Mohammad Al Jundi, and Ghaida Khasawneh at the Department of Statistics of Jordan for their support. We would like to thank Yichen Tu and Yoon Jung Lee for capable research assistance. We are grateful to the UK Department of International Development for funding assistance through its Strategic Research Program.
cities earn less controlling for household characteristics, overall city scale economies are very important for them. While people in the core have a density elasticity of 0.45 those in the fringe have a big extra kick and total elasticity of 0.75. Column 2 tells us that it is more overall city density that matters not density in the core. Column 3 then adds a local density measure. While people in the fringe still get an extra kick relative to those in the core from overall city density, we can not establish that local effects for those in the fringe are greater than those in the core. Column 4 reruns column 3 showing a similar core versus fringe differential response to overall city density, but it imposes the same effects in the fringe and core for local density.

The puzzle is the bigger role of city density externalities for fringe than core city residents. We thought this was because of sorting based on migration status, where migrants lack information and might benefit more from say information spillovers. Hence, we expected that the fringe would have a greater proportion of migrants (defined by having moved within the last 5 years) than the core within the LSMS sample. However, there is only a tiny difference: 16% vs 15%. Moreover, coefficients on migration status interacted with scale effects are insignificant and have no impact on the fringe results. So that leaves a puzzle. It certainly says that including the fringe of a city is important, but we do not have a ready answer for why economic density effects may be greater for people in the fringe. There certainly must be sorting on some dimension, where people in the fringe earn less for the same observables, but somehow this disadvantaged population benefits more from greater city density, even though by construction they live in less dense neighborhoods.<sup>15</sup>

# **6 Looking at firm productivity within a city: Kampala**

This section will highlight a critical aspect of the work in this paper. What we have investigated is the return to density in improving household incomes, including benefits from local neighborhood density within cities. Households are the ultimate beneficiaries of economic density in terms of wages and business incomes. In the literature as noted in the introduction there are a set of papers looking at income returns from agglomeration. How-

> 15These results apply to a sample excluding Nigeria where we do not know migration status. Results in the version of Table 9 without Nigeria are similar to the current Table 9.

26
| POLICY RESEARCH WORKING PAPER 3037

# Abstract

Pargal assesses the importance of the<sup>regulatory</sup> framework as a determinant of<sup>privatesectorinvestment</sup> in infrastructure. She uses recently<sup>compileddata on</sup> private and public sector investment<sup>in the water,power,</sup> telecommunications, railroads, and<sup>roadssectors</sup> between 1980 and 1998 in nine countries<sup>inLatin</sup> America. The author finds that<sup>the most significant</sup> institutional determinant of private<sup>investmentvolumes</sup> is the passage of legislation liberalizing<sup>the investment</sup> regime. This is important because<sup>it indicatesthatthe</sup> legal basis for reform is probably<sup>more critical</sup><sup>**in**</sup> determining the quality of the investment<sup>climatethan</sup> specific aspects of the institutional<sup>frameworkgoverning</sup> private sector participation. In<sup>accordance withintuition,</sup> the author's results indicate that<sup>governmentactionto</sup> increase regulatory certainty and<sup>minimizetheperceived</sup> risk of expropriation through the<sup>establishment of</sup> independent regulatory bodies is<sup>acriticaldeterminantof</sup> the volume of private investment<sup>flows.Shealso finds</sup> that the general relationship of<sup>privateto public</sup> investment is one of substitutability.

This paper-a product of the<sup>PolicySupportDivision,CorporateSecretariat-ispartofalargereffortintheBankto</sup> understand the role of institutions<sup>**in**development. Copies of the paperare available freefrom the World Bank,1818H</sup> Street NW, Washington, DC 20433.<sup>PleasecontactSheoliPargal,room MC11-153,telephone202-458-1951,fax 202-</sup> 522-1630, email address spargal@worldbank.org.<sup>PolicyResearchWorkingPapersarealsopostedontheWebat</sup> http.//econ.worldbank.org. April 2003.<sup>(43pages)</sup> _The Policy Research Working_<sup>_Paper Series disseminates the findings of_</sup><sup>**_ivork_**</sup><sup>_inprogress to encourage the exchangeof ideas about_</sup> _development issues. An_ **_objective_** _of_<sup>_the series is to get the findings out quickly,even_</sup><sup>**_if_**</sup><sup>_the presentations are less than fully polishedThe_</sup> _papers carry the names of the auithors_<sup>_and should be cited accordingly. The findings, interpretatiozs, and conclusions expressed_</sup><sup>**_in this_**</sup> _paper are entirely those of the authors._<sup>_Theydo not necessarily represent theviewof theWorld Bank,_</sup><sup>**_its_**</sup><sup>_ExecutiveDirectors, or the_</sup> _countnes they represent._ Produced by the Research Advisory<sup>Staff</sup>
to implement the national PMT. Initial discussions between the government, development partners and humanitarian actors revealed a division in terms of concepts, language and philosophy. While the humanitarian programs use vulnerability, the SSN uses monetary poverty for eligibility assessment and the targeting models between the two differ. However, the technical work summarized in this note allowed initial work to begin and momentum and discussion to ensue. Genuine concerns over data sharing issues were able to be overcome through the sharing not of underlying data but of pseudo-PMT models which captured the commonalities of targeting approaches without running afoul of data privacy.

This section outlines the MoLSA eligibility criteria and compares it to the current humanitarian approach. It then proposes a PPMT tool to compare these two methods given the lack of common data set.

# 3.1 MoLSA Eligibility

According to the new 2014 Social Protection Law, only households below the poverty line are eligible for social assistance support. The poverty line in 2017 was IQD 110,881 per person per month (Sharma & WaiPoi, 2019). By this line, national poverty was 20 percent in 2017-18 (Sharma & Wai-Poi, 2019),<sup>6</sup> slightly above the 18.9 percent measured directly in 2012 (World Bank, 2014) but a decline from 22.5 percent in 2014, and 41.2 percent in affected governorates, which was estimated indirectly by simulation methods (Krishnan & Olivieri, 2016). Thus, the current 1.36 million SPN beneficiaries (IMF, 2021) are less than the estimated number of poor in Iraq, highlighting the legal (but not fiscal) space for expanding the program. A full revision of the poverty line and poverty rate will be done after the implementation of the third round of the Iraq Household Socioeconomic Survey (IHSES).

In practice, MoLSA does not observe potential household beneficiaries’ poverty status, as this would require administering an intensive, time-consuming and expensive household expenditure survey which is not practical to be conducted for all potentially eligible households. Instead, the MoLSA determination of eligibility is based on the PMT scoring system and the application process has the following steps:

1. Application:

- a. Apply on the MOLSA website, screening questions determine if a home visit is warranted;

- b. Applications process closed May 2016, with 340,000 waiting to go for MOP scoring.

2. Home visits:

- a. For those households passing the screening question, social workers visit the home and collect data on a range of indicators as advised by the PMT formula.

3. Household scoring:

- a. Ministry of Planning (MOP) estimates household per capita consumption using the PMT indicators;

- b. Models updated when new national household survey data available.

4. Eligibility determined:

- a. Households with estimated per capita consumption under the poverty line.

> 6 The 2017/18 Survey of Well-Being via Instant and Frequent Tracking (SWIFT) could not be implemented in 14 of the 120 districts due to continuous insecurity. The direct poverty estimate from the data without simulating for the missing districts is 18 percent.

5
Table 6: Explanatory variables used in labor force participation equation

|VARIABLE|RATIONALE FOR INCLUSION|
|---|---|
|_Shock_||
|Unemployment rate: male national unemployment<br>rate by quarter|proxy for labor demand and labor<br>force entry as insurance|
|fall in household income|decrease in reservation wage|
|fall household earnings:<br>* 0-9%<br>* 10 - 24%<br>* 25 - 49%<br>*_50_- 74%<br>* 75 - 100%|to consider the importance of the<br>magnitude of the fall in household<br>income|
|*spouse involuntarily OLF: dummy = 1 if<br>husband was ILF in period 1 but was forced out of<br>his job by period 5|proxy for a fall in family<br>employment|
|* number of workers in household involuntarily|proxy for a fall in family|
|OLF (less spouse)|employment|
|_Demographics_||
|age: reported age|proxy for experience (human capital<br>theory)|
|school: reported years of formal education|human capital theory|
|<br>married- dummy =1 if married and = 0 if in a<br>consensual union|<br>balance of power in the household<br>(Fleck 1983)|
|*separated: dummy =1 changed from married or<br>consensual union to single between the periods|fall in family income|
|_Household structure_||
|young female adult: number of women in the<br>household age 18-64|more adult females may substitute<br>for female hh head in market or<br>house work|
|*change in number of young female adults|change in time constraints|
|old female adults: number of women in the<br>household age 65+|more older females may substitute<br>for the head wife in household work<br>OR incur costs on the head-wife's<br>time|
|young male adult: number of men in the<br>household age 18-64|more adult males may substitute for<br>female hh head in market work|
|*change in the number of young male adults|change in hh income constraints|
|<br>old male adult: number of men in the household<br>age 65+|<br>more older males increase<br>household expenses OR incur costs<br>on the head-wife's time|
|*newborn: (or wife) gave birth between periods 1<br>and 5|Additional constraint on time|
|children age 1-5: number of children in the|more young children limit time|
|household between the ages 1 and 5|available for market work|
|*change in young children: change in the number|Change in head wife's time|

37
WPS3709

# **How Well Do Institutional Theories Explain Firms’ Perceptions of Property Rights?**

## **Meghana Ayyagari Asli Demirgüç-Kunt Vojislav Maksimovic***

### **Revised: March 2006**

**Abstract:** We examine how well several institutional and firm-level factors explain firms’ perceptions of property rights protection. Our sample includes private and public firms which vary in size from very small to large in 80 countries. Together, the institutional theories we investigate account for approximately 50 percent of the country-level variation, indicating that the literature is addressing first-order factors. Firm-level characteristics, such as legal organization and ownership structure, are comparable to institutional factors in explaining variation in property rights protection. A country’s legal origin predicts property rights variation better than its religion, its ethnic diversity, or natural endowments. However, these results are driven by the inclusion of former Socialist economies in the sample. When we exclude the former Socialist economies, legal origin explains considerably less than ethnic fractionalization.

### **Keywords: Law, Property Rights, Variance Decomposition JEL Classification: D23, K4, C5**

### World Bank Policy Research Working Paper 3709

_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._

### **________________________________________________________________________**

*Ayyagari: School of Business, George Washington University; Demirgüç-Kunt: World Bank; Maksimovic: Robert H. Smith School of Business at the University of Maryland. We would like to thank Thorsten Beck, Murray Frank, Phillip Keefer, Leora Klapper, Gordon Phillips, Colm Kearney, Randall Morck and seminar participants at Rutgers, George Washington University, University of Maryland, and NBER for their suggestions and comments.
men, and the gap stays at those levels at higher income quantiles. Therefore, while for the majority of those at the top 1% women and men have similar income composition in terms of labor vs. capital, women’s income is more dependent on capital income at the very top (Atkinson _et al._ , 2018, Bobilev _et al._ , 2019).

# **8 Conclusion**

In this paper we have used rich administrative tax records, including detailed shareholder list of all corporations, to offer new measures of top income shares in Honduras.

Our main results can be summarized as follows. First, we document very large top income shares for Honduras – the top 1% receive approximately 30% of total pretax net national income. We provide evidence not only that the inequality levels in Honduras are among the highest in the world (for which detailed measurements are available), but also that they persist with little change over two decades. Second, we also show the predominance of capital income at the top of the distribution, and particularly of undistributed corporate profits. The ability to assign corporate profits to individual shareholders is only possible due to the construction of a shareholder dataset for the majority of corporations in the country. We also show that this information matters: using standard methods in the literature, where undistributed profits are distributed according to other distributed income, substantially underestimates income concentration within the top 0.1%. Finally, the panel dataset nature of our tax records also allows us to document a high persistence of the same individuals at the top of the distribution over time.

Our estimates are obtained following the most recent literature on measuring top incomes, rendering them broadly comparable to several similar efforts. Throughout the paper, we carefully document the methodological decisions we take and caveat the limitations of our approach - more generally, all estimates of top incomes combining survey data, tax records and macroeconomic national accounts carry a significant degree of uncertainty. Nonetheless, all our estimates are consistent in showing that real inequality levels are much higher and persistent over time than what survey data alone would indicate. The debate over the inequality levels and trends in Latin America would benefit from additional similar efforts throughout the region.

20
The Czech Republic was not the only country that permitted for sizeable appreciation of its currency, inflation target undershooting, or both in the pre-crisis period. Similar patterns could have been observed in other inflation targeters, for example, Canada, Korea, Norway, Slovakia, Sweden and Switzerland. The exchange rate obviously played a very important role in the monetary conditions in some small economies in the pre-crisis years. Positive outcomes in terms of price and financial stability were achieved in the countries in which central banks responded to exchange rate pressures broadly in a flexible inflation-targeting style. In particular, a strategy of reacting to the appreciation pressures pragmatically by cutting policy rates a bit and simultaneously allowing for some appreciation worked well.

**Chart 17. Currency Appreciation in Selected Inflation-Targeting Economies**

<!-- Start of picture text --> 110 120<br>110<br>100<br>100<br>90 90<br>80<br>80<br>70<br>70 60<br>50<br>60<br>CAD/USD KRW/USD<br>MYR/USD NZD/USD<br>AUD/USD<br>CZK/EUR SKK/EUR<br>1/01 1/02 1/03 1/04 1/05 1/06 1/07 1/08 1/09 1/10<br>1/01 1/02 1/03 1/04 1/05 1/06 1/07 1/08 1/09 1/10<br><!-- End of picture text --> Note: CAD - Canadian dollar, USD - United States dollar, KRW - Korean won, MYR - Malaysian ringgit, NZD - New Zealand dollar, AUD - Australian dollar.

Note: CZK - Czech koruna, SKK - Slovak koruna, EUR - euro. Source: Thomson Datastream. Note: January 2001 = 100. Decline means appreciation.

The explanation for the success depicted above is not a straightforward one. Basically, central bankers in small and open economies have been much more willing to accept that credit booms and asset-price bubbles can emerge without signs of inflationary pressures and that inflation measured in terms of consumer prices has not always signaled a buildup of imbalances in the economy. In some countries they felt—partly as a result of their own experience—that strong credit expansion and increasing asset prices preceded almost all banking crises and the majority of deep recessions. Some of them could also easily imagine a realistic scenario in which a bubble builds up without visible signs of inflationary pressures. Such a scenario can arise when higher economic growth creates excessively optimistic expectations about the future of the economy that lead to nominal appreciation of its currency. In such a situation, very low inflation can prevail for a long time even under rapid credit growth and asset price acceleration. When agents recognize that the real situation is not so rosy, and open inflation pressures subsequently appear, it may be too late for monetary policy to react. In addition, excessive demand, especially in small economies, may for a long time be reflected in rising current account deficits rather than in inflationary pressures.

central banks maintained much higher rates. In these countries, however, the maintaining of higher domestic interest rates resulted in households and corporations switching to foreign currency loans, which further increased the risks to financial stability. See also Čihák and Mitra (2009).

13
Assume that a household head � is confronted with the decision (denoted by ��) of whether or not to migrate to a different location. He will migrate only if the benefits expected from migrating are greater than the gains expected from not changing location (which can be normalized to zero for convenience). Let us indicate with ��∗ the unobservable latent variable measuring the gains deriving from the decision to migrate, this decision can be written as follows: where �������� is our exclusion restriction, the share of the population aged 15 and above who was not born in the village (or town) they resided in at the time of the 1984 Census, �� is a set of households’ and heads’ characteristics, and �� are district level controls. Household characteristics are assumed to affect the probability of migration; migration is a life-cycle event and as such young people and males are more likely to participate. Following the human capital model, education and skills are likely to affect the probability of migrating as more educated individuals have more chances to get high-paying employment opportunities in destination areas. For these reasons, we include gender, a second-degree polynomial in age, educational attainment, household size, household demographic (share of household members aged between 4 and 14, between 15 and 64 (separately for males and females), and 65 and above) and labor market structure (share of working-age household members who are employed and who are unemployed), current location of residence (urban/rural), and region of birth. The inclusion of region of origin fixed effects implies that we compare non-migrants with migrants from the same region of origin. As explained above, among the district level variables measured in 1984 we include the average number of years of schooling of the population aged 6 and above, the Gini index of years of schooling among the population aged 6 and above, the share of the population aged 6 and above with no schooling and the share of the employed population aged 10 and above engaged in agriculture measured (in logarithm).

Being a migrant is a binary variable and we estimate the selection equation via a probit model. The outcome of interest is the consumption level of migrant and non-migrant households. Consumption is a continuous variable constructed as the sum of total annual household consumption expenditure per adult equivalent, and we use an ordinary least squares estimator.

# �� �����������������������,

�� is the logarithm of consumption level of household _i_ , �������� is a dummy variable taking the value 1 if a household is headed by a migrant and 0 otherwise. We estimate the selection and consumption equations using a multi-equation mixed system that makes use of a conditional mixed process (CMP) estimator and fits a seemingly unrelated regression (SUR) simultaneous equation 13