rafmacalaba/datause-encoder-data huggingface dataset:
region A are expensive three-bedroom apartments. Both mean and median values of housing rents are indeed lower in region B than in region A. If one assumes housing stock is unlimited like other goods, then the hedonic regression approach may work. Otherwise, it would be better not to control for quality differences in measuring housing costs in spatial price adjustment. This statement sounds contradictory to our emphasis on the detailed product specification information in other goods and services. This is because housing is a unique case in which its supply and stock are limited in a given market.

**Table 2. A simple example of housing** **<u>price index</u>**

||Reg<br>|ion A<br>|Reg<br>|ion B<br>|
|---|---|---|---|---|
||Units|Rent ($)|Units|Rent ($)|
|One-bedroom|10|1,000|40|1,500|
|Two-bedroom|10|3,000|10|4,000|
|Three-bedroom|40|5,000|10|6,000|
|Price index based on hedonic regression|60|1,000|60|1,310|
|Mean rent|60|4,000|60|2,670|
|Median rent|60|5,000|60|1,500|

_Note:_ Number of housing units is expressed in thousands.

# **2.3. What are the alternatives to the standard price index approaches? How are they better?**

With the aforementioned methodological discussion in mind, we introduce here the CPD approach based on the CPI price data. Before getting there, however, we briefly touch upon another approach in spatial adjustment, which calculates a spatial price index based on multiple poverty lines.

## _Multiple poverty line approach_

An alternative and less common approach to measuring poverty is to construct regional poverty lines that reflect regional differences in the costs and needs to achieve the threshold utility. Poverty is measured by comparing each household’s nominal (that is, not spatially deflated) consumption aggregate to the region’s poverty line.<sup>15</sup> An advantage of this approach is that regional price levels of non-food items can be flexibly calculated. Since non-food allowance is typically determined based on the expenditure shares of households around food poverty lines (Ravallion and Bidani 1994), price data for non-food items is not required. When analyzing inequality or comparing household welfare across regions, one can construct a spatial price index by taking ratios of regional poverty lines. Due to the complexity of the methodology, however, regional poverty lines are used in only a few countries. The analysis of this approach is outside the scope of this paper.

## _CPD approach_

A challenge in spatial comparison of prices is that some items are not sold in some regions, and therefore, those prices are missing when calculating a price index. The CPD approach was originally developed to handle such missing values in price observations across regions (Rao 2004, 2005; Summers 1973).

15 Examples include Ravallion and Lokshin (2006) for the Russian Federation; Jolliffe, Datt, and Sharma (2004) for the Arab Republic of Egypt, and Marivoet and de Herdt (2015) for the Democratic Republic of Congo.

11
**Table A2** Predictors of Boys Participation in Boys Soccer Treatment

||(1)|(2)|
|---|---|---|
||**Contacted**<br>**by GRS**|**Enrolled**<br>**in GRS**|
|Enrolled in school|0.076|0.133**|
||(0.048)|(0.058)|
|Own house|0.037|-0.054|
||(0.040)|(0.040)|
|Never talk to dad about SRH|-0.026|-0.000|
||(0.050)|(0.056)|
|Age|0.027***|0.001|
||(0.004)|(0.004)|
|House has electricity|-0.021|0.033|
||(0.043)|(0.041)|
|Number of household members|0.001|0.040**|
||(0.015)|(0.018)|
|Iringa|-0.246***|0.078|
||(0.081)|(0.079)|
|Mbeya|-0.084|0.131**|
||(0.083)|(0.062)|
|Outcome mean|0.679|0.352|
|Observations|787|787|

_Notes._ In column 1, the outcome is an indicator that the GRS team had a record of contacting the male in our study sample, and, in column 2, the outcome is an indicator that the GRS team recorded enrolling the male in our study sample. The coavariates in the rows are a binary indicator that the male is enrolled in school, a binary indicator that the male’s household owns the house he lives in, a binary indicator that the male never talks to his dad about SRH topics, age of the male, a binary indicator that the male’s house has electricity, number of household members, and indicators for region of residence. Standard errors, clustered at the club level, are presented in parentheses below the coefficient estimates. .01 ***, .05 **, .10 *. _Source._ Male baseline data, restricted to males in the _Boys_ treatment arm.

61
# **Figure 8:  Comparing naïve and floor area adjusted population densities (FAPD) across 400 global cities**

Figure a. Population density and city income, 2015. (population per km2 of built-up area, log scale) <!-- Start of picture text --> 150000<br>100000<br>Mumbai<br>50000<br>Kinshasa Karachi Hong Kong<br>Dhaka<br>Lagos Beijing<br>10000 Shanghai<br>Tokyo<br>London<br>5000<br>2000 Dallas<br>City income in 2015, log scale<br>Density (population per sq. km of built-up), log scale<br>500 1000 5000 10000 40000<br><!-- End of picture text --> Figure b. Floor Area Adjusted Population Density (FAPD) and city income, 2015 (population per km2 of floor space, log scale) <!-- Start of picture text --> 150000<br>100000<br>30000<br>Kinshasa<br>Karachi<br>Mumbai<br>10000 Dhaka<br>Lagos LondonHong Kong<br>5000<br>Tokyo<br>ShanghaiBeijing<br>2000<br>City income in 2015, log scale<br>Density (population per sq. km of floor space), log scale<br>500 1000 5000 10000 20000 40000<br><!-- End of picture text --> Source: Analysis based on DLR WSF 3D data set The FAPD in Figure 8b is a reasonable indicator of livability. Hong Kong’s FAPD is in line with that of a highincome city like London or Tokyo. FAPDs for low-income cities such as Kinshasa and Mumbai are also lower than their naïve population densities, with the correction making more of a difference for specific neighborhoods that have taller structures. While the FAPD makes intuitive sense, more work is needed to get better estimates of building heights. The DLR WSF3D data make a first contribution to such measurement. There are many measurement errors in the data coming from the ability of satellite sensors to measure height as well as noise introduced in the aggregation of the data collected at 12mx12m resolution. However, analysis of these data for the city of Dhaka shows remarkable correlation with predictions of heights based on productivity measures estimated through a general equilibrium urban model (Sturm, Takeda and Venables 2021b).

14
WPS3698

# Searching for the Economic Gradient in Self-Assessed Health

## Michael Lokshin and Martin Ravallion<sup>1</sup>

_Development Research Group, World Bank 1818 H Street NW, Washington DC, 20433 USA_

**Abstract:** Can self-assessments of health reveal the true health differentials between “rich” and “poor”?  The potential sources of bias include psychological adaptation to ill-health, socioeconomic covariates of health reporting errors and income measurement errors.  We propose an estimation method to reduce the bias, by isolating the component of self-assessed health that is explicable in terms of objective health indicators and allowing for broader dimensions of economic welfare than captured by current incomes.  On applying our method to survey data for Russia we find a pronounced (nonlinear) economic gradient in health status that is not evident in the raw data.  This is largely attributable to the health effects of age, education and location.

**JEL:** D63, I12, I31

**Keywords:** Self-assessed health, subjective welfare, gradient, measurement errors, Russia **Correspondence:** M. Ravallion, World Bank, 1818 H Street NW, Washington DC, 20433 USA. mravallion@worldbank.org

World Bank Policy Research Working Paper 3698, September 2005; Revised July 2006 _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 The authors are grateful to Mingshan Lu, Vassilis Hajivassilou and participants at the Fifth World Congress of the International Health Economics Association for their comments on an earlier version of this paper.
the consequences of regulatory barriers against entrepreneurship are seen, not in young firms, but in older firms, who grow more slowly and to a smaller size. Desai et al. (2003) uses the same database and a cross-country approach and find complementary evidence that entry regulations have a negative impact on firm entry. Scarpetta et al. (2002) using firm-level data from OECD countries to analyze firm entry and exit finds that higher product market and labor regulations are negatively correlated with the entry of small and medium sized enterprises (SMEs) in OECD countries.

A number of studies have also used firm surveys to study the determinants of firm growth. Using a survey of manufacturing firms in Cote d'Ivoire, Sleuwaegen and Goedhuys (2002) find that younger firms grow faster than older firms, but larger entrants experience greater growth opportunities that improve over time. Yet a shortcoming of most studies in low- and middleincome countries is their dependence on survey data. The reliance on survey data poses limitations since firm entry is typically not observed, the data is biased towards surviving firms, and time series are short. Mead and Liedholm (1998) rely on survey data from five Eastern and Southern African countries (Botswana, Kenya, Malawi, Swaziland, and Zimbabwe) as well as the Dominican Republic to examine the magnitude and determinants of firm births, deaths, and expansions of micro and small enterprises (MSEs). The authors find that the annual rate of new MSE start-ups averages over 20%, and that the vast majority of new firms were one-person establishments. Shiferaw (2007, 2009) and Bedi and Shiferaw (2009) are notable exceptions to the majority of African firm-level studies that depend on survey data and instead utilize manufacturing census data. However, in their studies of Ethiopian establishments the authors still face stock sampling (survivorship bias) problems and are limited to the manufacturing sector.

Another important question in the literature is the determinants of firm survival. For example, evidence from Portuguese manufacturing firms suggests that drivers of a firm's survival include start-up size, with larger firms having a higher probability of survival, and fast growing industries having a positive effect on firm duration. Furthermore, the probability of firm failure decreases with the age of the firm, consistent with the view that firm entry is a process of selection and learning (Mata and Portugal, 1994). More recently, Frazer (2005) and Soderbom et al. (2006) use manufacturing data from the Regional Program on Enterprise Development (RPED) surveys of Ghana, Kenya, and Tanzania to analyze the role of productivity in firm 5
Table 7: Children’s educational efficiency <u>(OLS)</u>

|Dependent variable:||One if pas<br>|s<br>|
|---|---|---|---|
||D|ecember 2|014|
||(a)|(b)|(c)|
|All products|-|-0.062<br>|-0.640*<br>|
|||(0.049)|(0.351)|
|One product|-|-0.017|-0.446|
|||(0.051)|(0.346)|
|Study at school|-0.001|-0.001|-0.001|
|(per-day minutes)|(0.001)|(0.001)|(0.001)|
|Study at home<br>|0.000<br>|-0.000<br>|-0.001<br>|
|(day, per-day minutes)<br>|(0.001)|(0.001)|(0.001)|
|Study at home|0.000|0.001|0.001|
|(night, per-day minutes)<br>|(0.001)|(0.001)|(0.001)|
|One if attend|0.073|0.078|0.033|
|(February)|(0.048)<br>|(0.047)<br>|(0.078)|
|One if attend|-0.072*|-0.069*|-0.076|
|(April)|(0.038)|(0.037)|(0.054)|
|One if attend<br>|0.010<br>|0.010<br>|-0.073<br>|
|(August)|(0.038)|(0.039)|(0.056)|
|All products||||
|_×_ Study at|-|-|0.001|
|school|||(0.001)|
|_×_ Study at|-|-|0.005**|
|home (day)|||(0.002)|
|_×_ Study at|-|-|-0.002|
|home (night)|||(0.002)|
|_×_ One if attend|-|-|0.110|
|(February)|||(0.133)|
|_×_ One if attend|-|-|0.008|
|(April)|||(0.075)|
|_×_ One if attend|-|-|0.085|
|(August)|||(0.099)|
|One product||||
|_×_ Study at|-|-|0.000<br>|
|school|||(0.002)|
|_×_ Study at|-|-|-0.001|
|home (day)|||(0.003)|
|_×_ Study at|-|-|0.002|
|home (night)|||(0.002)|
|_×_ One if attend|-|-|0.177|
|(February)|||(0.120)|
|_×_ One if attend|-|-|-0.028|
|(April)|||(0.125)|
|_×_ One if attend|-|-|0.214**|
|(August)|||(0.088)|
|R-squared|0.157|0.159|0.184|
|No. of obs.|617|617|617|
|Baseline control|No|No|No|
|School FE|Yes|Yes|Yes|
|Village FE|Yes|Yes|Yes|

Notes: (1) Figures ( ) are standard errors. *** denotes significance at 1%, ** at 5%, and * at 10%. (2) Standard errors are robust to heteroskedasticity and clustered residuals within each school-grade.

31
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|_|TTTTITTIIITT |ttcainsfronITTt.otbag2 | ||II :..1.7<br>SERBS EERE E..<br>t4 forEU<br>PtppttT tt tttt a<br>ona Co<br>ao Ci Bie<br>a0 CC<br>Sh SREER Ej<br>Losses trom -0.06 to 0<br>ee St mr 6<br>Scaling of supply elasticities for African and Rest of World firms<br><!-- End of picture text -->
countries. Pre-tax and post-tax subsidies add up to 0.7 and 2.9 percent of GDP in 2011, or about US$490 billion and US$2 trillion, respectively. Dollar equivalent values are provided in Table 5. The wide range of the IMF estimates underscores the sensitivity to how externalities are quantified. Despite many differences in calculation methods and coverage, the IMF’s total for pre-tax subsidies in 2011 is similar to that obtained by the IEA, both in the vicinity of US$500 billion, although there are some differences in the breakdown.

**Table 5 IMF energy subsidy estimations for 2011 in nominal U.S. dollars**

|**Type**|**Petroleum**|**Naturalgas**|**Coal**|**Electricity**|**Total**|
|---|---|---|---|---|---|
|Pre-tax|220|116|7|150|492|
|Post-tax|727|376|709|179|1,990|

_Source_ : Clements et al. 2013; IMF staff.

Once producer subsidies are included, global fossil fuel subsidies may have been about 1 percent of GPD in the past three years. If oil prices average US$55–75 a barrel in 2015, this figure could fall correspondingly, given the relative weight of oil in total subsidies.

# **Concluding Remarks**

A confluence of factors in recent years has intensified policymakers’ attention to measuring and assessing fossil fuel subsidies. Analysis of fuel subsidies can achieve several outcomes:

- **Policy assessment** . The _intended_ economic and social outcomes associated with the subsidy policies can be evaluated against the _actual_ outcomes. Such findings can help weigh economic effectiveness, efficiency, and equity of the existing subsidy policies against alternative options to achieve the same goals.

- **Decision-making relevance and policy alignment** . Timely availability of data on various forms of subsidies can better inform fiscal, sector, trade, environmental, and other policy making and debates.

- **Efficiency improvement in resource allocation and sector operations.** Redirecting existing subsidies and sending more appropriate price signals can improve resource allocation in the economy, efficiency in the energy sector, and the quality of government spending.

Such assessment begins with a good understanding of the scale of the subsidies, enabled by sustained and comprehensive subsidy tracking.

This paper suggests that the question facing governments with respect to evaluating energy subsidies is not a choice between different methods, but rather how to make the best use of available methodological approaches in order to leverage available data and provide timely and cogent insights on the policy question at hand. If the most pressing issue is how to reform prices, price-gap data are essential. Data needed for price-gap calculations are fairly well-known. It is important to pursue data collection, consolidation, and validation proactively. The price increases needed to eliminate subsidies estimated by the price-gap approach can be used as inputs in partial or general equilibrium models.

36
Love, Inessa, Lorenzo A. Preve and Virginia Sartia-Allende, 2006. Trade Credit and Bank Credit: Evidence from Recent Financial Crisis. World Bank Working paper.

Marotta, Giuseppe, 2005. Is Trade Credit More Expensive than Bank Credit Loans? Evidence from Italian Firm-Level Data. _Applied Economics_ 37(4), 403-416.

McMillan, John and Christopher Woodruff, 1999. Interfirm Relationships and Informal Credit in Vietnam, _The Quarterly Journal of Economics_ 114, 1285-1320.

Mian, Shehzad L. and Clifford W. Smith, 1992. Accounts Receivable Management Policy: Theory and Evidence. _Journal of Finance_ 47, 169-200.

Petersen, Mitchell and Raghuram G. Rajan _,_ 1995 _._ The Effect of Credit Market Competition on Lending Relationships. _Quarterly Journal of Economics_ 110, 407-443.

Petersen, Mitchell A. and Raghuram G. Rajan, 1997. Trade Credit: Theory and Evidence. _Review of Financial Studies,_ 10 (3), 661-691.

Smith, J. K., 1987. Trade Credit and Informational Asymmetry. _Journal of Finance_ , 42, 863–872.

Stohs, M., and Mauer, D., 1996. The Determinants of Corporate Debt Maturity Structure. _Journal of Business_ , 69, 279-312.

Summers, Barbara and Nicholas Wilson, 2002. Trade Credit Terms Offered by Small Firms: Survey Evidence and Empirical Analysis. _Journal of Business and Finance Accounting_ 29, 317-335.

Van Horen, N., 2005. Do firms use trade credit as a competitiveness tool? Evidence from Developing Countries, World Bank Working Paper.

Wilner, Benjamin S. 2000. The Exploitation of Relationships in Financial Distress: The Case _of_ Trade _._ Credit. _The Journal of Finance_ 55, 153-178.

35
Policy Research Working Paper 5401

# **Abstract**

This paper uses model averaging techniques to identify robust predictors of sovereign default episodes on a pooled database for 46 emerging economies over the period 1980–2004. Sovereign default episodes are defined according to Standard & Poor’s or by non-concessional International Monetary Fund loans in excess of 100 percent of the country’s quota. The authors find that, in addition to the level of indebtedness, the quality of policies and institutions is the best predictor of default episodes in emerging market countries with relatively low levels of external debt. For emerging market countries with a higher level of debt, macroeconomic stability plays a robust role in explaining differences in default probabilities. The paper provides evidence that model averaging can improve out-of-sample prediction of sovereign defaults, and draws policy conclusions for the current crisis based on the results.

This paper—a product of the Economic Policy and Debt Department, Poverty Reduction and Economic Management Network—is part of a larger effort in the department to identify and address developing country vulnerabilities in the face of financial and economic crisis. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The authors may be contacted at lbandiera@worldbank.org, gvincelette@worldbank.org, and jcrespo@wu.ac.at.

_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
# **3 Data**

This section first describes the raw data and then the transformations that are made to each series before estimation. A subsection is dedicated to addressing some of the challenges inherent in working with the search query data from Google in emerging market countries. For each of Costa Rica, El Salvador, and Honduras<sup>2</sup> , there are two – categories of series that we will forecast data on aggregate consumer prices and their component series and staple food price data.

We obtained the consumer price data from the statistical office of each country. See Appendix A for details. The raw series are in levels and are not seasonally adjusted.

The food price data was obtained from the Global Information and Early Warning System on Food and Agriculture (GIEWS) from the Food and Agriculture Organization of the United Nations (FAO). The types of food that are available from GIEWS are particular to each country. We obtained every available series. Appendix A gives, for each country, the series names, appropriate region, and the units for which we have data available. These series are not seasonally adjusted.

To augment our forecasts, we have obtained Google Trends data on a number of search keywords. These keywords were chosen _ex ante_ with the belief that they contain relevant information that will allow us to use them as a proxy for consumer behavior and beliefs. Obtaining real-time insights into consumer behavior allows us to better predict price changes all other things equal. In some sense, the Trends data takes the place of traditional consumer-sentiment surveys. The keywords that we have chosen are listed in Table 1.

Each individual Google Trend series is relative and not an absolute measures of search volume. That is, the period in which the search interest of a keyword is highest within the dates of inquiry receives a value of 100. All other periods for an individual series are measured relative to this highest period. There is, therefore, no sense of how many people were searching for a term and the terms themselves are not comparable with each other. Furthermore, changes in Internet penetration and the use of Google, in particular, do not matter.

The following transformation is made to each price series before estimation to go from

> 2We could not acquire sufficient data on food prices or on search keywords for Belize, so it is omitted from discussion. Earlier versions of this paper contained every other country in Central America. However, given some of the data challenges discussed below, we chose to narrow our interest to three countries. We chose Costa Rica and El Salvador because they generally have good data availability from Google Trends. The quality of the data for Honduras, on the other hand, was found to be rather poor, so we included it to learn more about how the models perform under adverse data conditions.

4
# _UMhwivuN_ b. A _Amb C.mbie_

|_Conby_|_UnhI*_<br>_& Locaton_|_Yea_|_Lana_|_Acad_<br>_Staff_|_dent_|
|---|---|---|---|---|---|
|raq|Univerdty of<br>SuWahedin, Erbil|1968|A, E|5<br>541 F.T.|845<br>5,823|
||Uneity<br>of<br>Tecnobgy, Baghdad|1975|A.E|84E8S<br>29|84-5<br>7,993|
|Jordan|Betdlehem University<br>Bethlehm, wB|1973|A,E|84E85<br>94 F.T.<br>27 P.T.|845<br>1,574|
||Birit Unioqty<br>Bizdt.,WB|1972|A, E|8445<br>209 F.T.<br>2 P.T.|2,393|
||Unimsiy of Jorda,<br>A.mn|1962|A, E|5<br>630 F.T.<br>32 P.T.|-<br>11,123|
||Heba<br>University,<br>Hebr,<br>_we_|1980|A|8485<br>46 F.T.<br>3 P.T.|84-<br>1,830|
||Mu'ah Univehky,<br>Mu'lah|1981|A, E|c. 2D|c300|
||An-Najah Natiil<br>U.,<br>Nablus, ws|1977|A, E|84-85<br>214 F.T.|84-85<br>2,825|

a MOB-EMlnry of Edlation; MOHESR-Minslty of iga Education and Sdecntfic Researh b Ag-Agricultu; Ar-Achitectu A-Ans Cs-Coper Sc; D-Dendtisy; Ed-Educaton; E-Engwieg; Ft-Food **Techd4** LaLaas L-law M-Medfidie; P-Pbannac R-Refloom Studies; S-Scinces; T-Technolap Vm-Vtcnazy Med.

_40_