rafmacalaba/datause-encoder-data huggingface dataset:
Depending on the household’s ability to cope with income fluctuations, a negative income shock brought on by bad weather may translate into a reduction in consumption (Jacoby and Skoufias, 1998; Dercon and Krishnan, 2000). In theory, weather-related shocks are difficult to insure against because they affect a whole community, decreasing the ability to use informal risk-sharing within the community. However, households may be able to protect their basic needs from the adverse shock. It has been found for instance that food consumption is better insured than non-food consumption (Skoufias and Quisumbing, 2005).

Beyond the ability of households to cope with whether shocks, a stronger case could be made about the likely impacts of climate change on household welfare, if it were possible to establish that the relationship between rainfall shocks and consumption is non-linear. Weather–related shocks are predicted to take place more frequently because of climate change. This, however, does not imply that climate change and increased climatic variability is likely to have an overall negative effect on welfare. This is, because it is possible that the welfare benefits associated with positive shocks, such as the better crop yields associated with slightly more than normal rainfall, on average may “cancel out” the adverse effects of negative shocks. Thus, in this case where the relation between shocks and welfare is “linear”, on average, climate change may not have an impact on household welfare. On the contrary, climate change will have an impact on average, if, for example, negative shocks have a negative welfare impact that is larger, in absolute value, than the positive welfare impact of positive shocks. If this is the case, then there is need for action to protect households from negative shocks due to climate change. We illustrate this in figure 1.

# Figure 1 here

The grey dotted line shows a case where shocks have an impact that is linear: positive shocks and negative shocks have impacts of similar magnitude. The black dashed line represents a different case: negative shocks have an impact, of the same magnitude as that in the grey dotted line but positive shocks have no impact. With the linear impact –the grey dotted line - clearly the average of positive and negative shocks of similar magnitude will be zero. With the black dashed line however, the average of positive and negative shocks is negative and this is the case where climate change may be harmful for consumption.

## **Data and Descriptive Statistics**

We use the 2003, 2006 and 2009 waves of the Family and Income Survey (FIES). The FIES is conducted by the Filipino Statistical Office and is regionally representative. The three rounds are conducted in the same time in the year and the same consumption module was used in all three rounds, allowing for comparability across the surveys.

We merge the FIES with data built using the monthly rainfall records of 43 rainfall stations across the Philippines, obtained from the Philippines Atmospheric, Geophysical and Astronomical Services Administration (PAGASA). Figure 2 displays the location of the 43 6
unchanged across the two rounds. Mostly, they will be characteristics of the household head, such as the year of birth, educational attainment, ethnicity or religion and birth district. In addition, if the survey includes retrospective questions, these could be used, e.g., if the household owned a car or television at the time of the previous survey. As we have data for each year, we estimate the coefficient vector on these time-invariant explanatory variables. In practice we predict the round 1 income for households in the round 2 survey by ββ applying the coefficient vector resulting from the round 1 regression ( 1 ) to the characteristics of households in round 2 ( ). This prediction looks as follows for the observations of round 2: ββ ii2 xx ii ′ 1 . ii1 ii2 ii1 yy� = ββ�xx + εε̂ We turn first to the assumptions underlying this approach and will then discuss how estimates for the error term are obtained.

The method for estimating bounds on poverty transition quantities is predicated on two important assumptions. First, we need to assume that the underlying population from which the data are sampled is the same in both survey rounds. This implies that the method cannot be applied in cases where there are large shocks to the population, for example, if there were massive migration into or out of the country. More precisely, we want to ensure that the income model coefficients that we estimate in round 1 are good predictors for what the round 1 income of the households surveyed in round 2 would have been. Hence, to ensure stability of the reference population, we only include households whose heads are between 25 and 55 years in survey round 1.<sup>9</sup> Second, we need to make assumptions about the relationship between the error terms and . Specifically, we assume that these are, on average, positively correlated over time. In the presence of ii1 εε household fixed effects and persistence of shocks, this does not seem to be an unreasonable assumption. ii2 εε Where panel data are available, the correlation coefficient can be estimated directly, and indeed Dang et al. (2014) report values between 0.39 and 0.66 for the six countries they investigate.<sup>10</sup> We do not make ρρ further assumptions about the shape of the distribution of the error terms; in that sense, these estimates are non-parametric.

> 9 This age range is chosen so that formation of new households and dissolution of existing households should be at a minimum. Moreover, it ensures that in round 1 heads will generally have obtained their maximum level of education.

> 10 Dang et al. estimate a value of 0.50 for Indonesia (1997-2000) and values of 0.58 – 0.66 for Vietnam (all pairs of 2004, 2006 and 2008) (Dang et al. 2014, p. 120).

10
26 48. There are two possible ways of dealing with the second category of costs. The first is to simply treat them in the same way as collective common costs and charge them against all users. This has some attractions on grounds of simplicity and economic efficiency. The second way is to charge them against each group of common users. This is more equitable and may hence be more politically acceptable. Each of the grouped common costs can, at least in principle, be avoided by withdrawing service from that specific user group: either heavy vehicles, urban road users, or rural road users. To justify scoving them, each group should be willing to pay for the costs they incur: heavy vehicle; again in terms of ESA km and urban and rural road users in terms of passenger car equivalent (PCE) kilometers (which measures the road space they occupy).l8/ Finally, the collective common costs should be recovered from all users in a way which minimizes loss of consumer surplus.

# 6.2 Recovering Collective Coumnon Costs

49. The collective common costs shown in Table 4, column 5, are incurred on behalf of several groups of common users and the aim is to define a mark-up over base costs (VOCs and variable and grouped common costs) which: (i) generates sufficient revenues to cover all collective common costs; while (ii) minimizing the consequent loss of consumer surplus. This is similar to the classical tax problem in which the overall excess burden of the tax is minimized by equalizing the marginal excess burden of the last dollar of tax revenues raised from each commodity (Ramsey, 1927; Rosen, 1988). In the case of roads, this translates into minimizing the relative loss of consumer surplus per dollar of collective common costs covered by each user group. This is illustrated in Figure 6. In this figure D represents the compensated demand curve for one particular type of traffic. The curve **BC** refers to base costs and represents VOCs and the variable and grouped common costs set out in Table 4, columns (1) to (4). Without any mark-up, demand would be in equilibrium at point B, with output N at price P. The amount **T*P1,** the ad valorem mark-up over the base costs, represents the contribution (per veh km) which this type of traffic makes towards covering collective common costs. The deadweight loss per dollar of revenue raised through the increased user charge, S, is equal to the triangular deadweight loss area **ABC** divided by the contribution to collective common costs, **P"CAP'.** In other words: S **= 1/2*AC*(N-N')/AC'N' = 1/2-AN/N'** where **AN = (N-N').** 18/ PCEs **measure** the impact which vehicles have on **the** speed, **and** hen:e the cost, of other vehicles. They vary by type of road. **terrain and** condition of traffic.
The information gathered through the Health and Social Security Program of the Ombudsman Office ( _Programa de Salud y Seguridad Social de la Defensoría del Pueblo de Colombia_ ) points to an improvement in terms of effective health coverage—in particular, in HIV/AIDS. The number of tutelage actions<sup>45</sup> presented by citizens to the Colombian Ombudsman Office went up from 86,000 in 1999 to more than 400,000 in 2010. Those related to the exercise of health rights also went up, but to a lesser extent—increasing from 21,000 in 1999 to more than 94,000 in 2010.

However, as Figure 8 below shows, tutelage actions related to HIV/AIDS have been decreasing throughout these years. They went from 3,505 in 2003, to 1,910 in 2006, to 846 in 2008—reaching the lowest figure in 2010: 807.

**Figure 8: Evolution of Tutelage Actions related to HIV/AIDS, 2003-2010**

<!-- Start of picture text --> 450.000<br>400.000<br>350.000<br>300.000<br>250.000<br>Tutelas en<br>200.000<br>VIH/SIDA<br>150.000 Tutelas en Salud<br>100.000 Total Tutelas<br>50.000 General tutelages<br>0<br>Health tutelages<br>4.000<br>HIV/AIDS tutelages<br>3.000<br>2.000<br>Tutelas en<br>VIH/SIDA<br>1.000<br>0<br>2003 2004 2005 2006 2007 2008 2009 2010<br>2003 2004 2005 2006 2007 2008 2009 2010<br><!-- End of picture text -->

**Source:** Técnicas de Salud 2011, from data included in the series _La tutela y el Derecho a la Salud_ , published by the Defensoría del Pueblo between 2007 and 2011.

> 45 Data included in the series _La tutela y el Derecho a la Salud_ , published by the Defensoría del Pueblo between 2007 and 2011.

44
Our main objective in undertaking the factor analysis is to quantify the factors for each country, that is, compute “factor scores” on each factor. Importantly, we wish to accomplish this in a way that allow comparisons of the factor scores over time. To this end, the paper details four issues in the factor analysis of the KAM data in detail. The first is whether the KAM data should be factor-analyzed and what factor-analytic method may be most appropriate; the second is determining the optimal dimensionality of the data, that is, the number of factors to which the data may be adequately reduced; the third, and perhaps most important, is giving clear meaning to the factors. Each of the above issues is treated exhaustively in the paper.

If subsets of variables are correlated, then depending on the extent of the correlation, factor analysis is worth doing. A formal test shows that the KAM data are not just amenable to factor analysis but they greatly benefit from it. There are enough inter-correlations among the variables that the real information in the data can be distilled down to a smaller number of dimensions.

What is the optimal dimensionality to which the information contained in the variables can be reduced? Depending on the factor analytic method that is chosen, the answer is different. For example, in principal components analysis this is determined by the number of principal components required to explain, say, 95% of the total variance in the data. In “true” factor analysis (which we estimate using maximum likelihood) a formal chi-squared test or information criteria that measure fit in terms of explained intercorrelations, not just variance, are used to determine optimal dimensionality.

The most important contribution of the paper is that it gives political-economic meaning to the dimensions, whether they go by the name of “factors” or “principal components”. Ultimately, we hope to make factor analysis a useful policy tool to indicate warning signals 2
|= 0_, X_)<br>(5)<br>IV<br>0.060***<br>(0.023)<br>0.006<br>(0.031)<br>-0.029<br>(0.027)<br>-0.014<br>(0.031)<br>-0.014<br>(0.027)<br>0.000<br>(0.009)<br>0.000<br>(0.004)<br>-0.037**<br>(0.018)<br>-0.030**<br>(0.015)<br>Yes<br>) and (2) report<br>erence between<br>o report to have<br>reatment group<br> education and<br>istics, sub-zone<br>we run a probit<br>nifcance of the|
|---|
|1_, X_)_−E_(_Y |T_ <br>(4)<br>OLS<br>0.044**<br>(0.017)<br>-0.004<br>(0.023)<br>-0.011<br>(0.021)<br>-0.011<br>(0.024)<br>-0.024<br>(0.020)<br>0.003<br>(0.006)<br>-0.001<br>(0.003)<br>-0.026*<br>(0.014)<br>-0.022**<br>(0.011)<br>Yes<br>only. Columns (1<br>(4) report the diff<br>en households wh<br>cipation with the t<br>clude gender, age,<br>dwelling character<br>int signifcance, <br>st for the joint sig|
|_E_(_Y |T_ =<br>(3)<br>OLS<br>0.054**<br>(0.021)<br>0.006<br>(0.023)<br>0.015<br>(0.025)<br>0.017<br>(0.033)<br>-0.018<br>(0.023)<br>-0.004<br>(0.009)<br>-0.000<br>(0.002)<br>-0.034**<br>(0.014)<br>-0.033**<br>(0.012)<br>No<br>for respondents<br> Columns (3) and<br>difference betwe<br>ng program parti<br>ses. Controls in<br>access to water,<br>To control for jo<br>values of an F-te|
|(2)<br>Treatment<br>0.908<br>[0.289]<br>0.213<br>[0.410]<br>0.170<br>[0.375]<br>0.262<br>[0.440]<br>0.113<br>[0.316]<br>0.017<br>[0.131]<br>0.002<br>[0.050]<br>0.035<br>[0.183]<br>0.037<br>[0.189]<br>old, data available<br>ations in brackets. <br>n (5) estimates the<br>dn’t by instrumenti<br>ported in parenthe<br>nformation about<br>g in the village). <br>s and we report p-|
|(1)<br>Control<br>0.854<br>[0.353]<br>0.207<br>[0.405]<br>0.154<br>[0.362]<br>0.245<br>[0.431]<br>0.131<br>[0.337]<br>0.021<br>[0.144]<br>0.002<br>[0.050]<br>0.068<br>[0.253]<br>0.070<br>[0.255]<br>tion per househ<br>, standard devi<br>odel2). Colum<br>d those who di<br>ge level are re<br>e and religion, i<br>f women livin<br>oups of variable|
|Mosquitoes<br>Contaminated water/Unhygienic surroundings<br>Fatigue/Too much time in the sun<br>Poor diet/Eating dirty food<br>Tall grass/Wet areas<br>From person to person<br>During outbreaks<br>Other reasons unrelated to malaria<br>Respondent doesn’t know<br>Controls<br>Note: *** p_<_0.01, ** p_<_0.05, * p_<_0.1. One observa<br>sample means restricted to control and treatment group<br>treatment and control groups using OLS regression (m<br>received the spraying campaign in the last 5 months an<br>indicator (model 3). Standard errors clustered at villa<br>marriage status of the respondent, household size, trib<br>dummies and village characteristics (size and share o<br>regression of the treatment indicator on the selected gr<br>coeffcients on the selected variables.|

17
extent of local presence is particularly high in Latin America (especially Mexico and Bolivia) where in many countries the share of local presence exceeds 75 percent, but never reaches 100.

As shown in Table 3, U.S. banks appear to be active in one way or the other in all but 5 countries in our sample (i.e., Armenia, Burkina Faso, Burundi, Madagascar, and Rwanda). Furthermore, contrary to the case of Spanish and Italian banks, U.S. banks have some form of local presence in more than three-quarters of the sample and those countries where U.S. claims are exclusively cross-border are mainly small developing economies (with the exception of Saudi Arabia). Finally, relative to Spain and Italy, it is harder to detect any regional pattern in the share of U.S. bank local foreign claims, since countries with high shares include both developed and developing countries in almost every region.

# **III - Empirical Methodology**

The empirical analysis conducted in this paper has two objectives: (1) to study the determinants of the mix of foreign claims and (2) to examine the implications of this mix for the stability of foreign bank financing. Below, we discuss the empirical strategies pursued to accomplish these objectives. Because the time span covered by the data is relatively short and since many of the variables of interest do not vary over time, we focus on explaining differences across countries. Therefore, we consider only the average share of local foreign claims across countries and, for each country, the volatility of total foreign claims over the entire sample period.

Taking into account the existing empirical international banking literature and borrowing from the theoretical framework used in related trade and multinational firm studies, we model 10
the APO Seminar on Nonfarm Employment Opportunities in Rural Areas, Philippines, published by Asian Productivity organization 1-2-10 Hirakawacho, Chiyoda-ku, Tokyo 1020093, Japan.

Paulson, Anna L., and Robert Townsend, (2004), “Entrepreneurship and financial constraints in Thailand” _Journal of Corporate Finance_ , 10, P.229– 262.

Rajesh Raj, Seethamma Natarajan, November 2007, “Technical Efficiency in the Informal Manufacturing Enterprises: Firm level evidence from an Indian state”, MPRA Paper No. 7816, posted 18. March 2008.

Reardon, T. 2000. “Interactions Between the Agricultural System and Non-agricultural Activities of Farm Households in Developing Countries,” in J-P Colin and E.W. Crawford (eds.) Research on Agricultural Systems: Accomplishments, Perspectives, and Issues. Huntington, NY: Nova Science Publishers, Inc., 163-178.

Samphantharak, Krislert and Robert M. Townsend. 2011. “Measuring the Return on Household Enterprise: What Matters Most for Whom?” Working Paper, MIT 2011.

Sawada, Naotaka, and Jian Zhang. 2012. “Promoting the Rural Farm and Nonfarm Businesses, Evidence from the Yemen Rural Investment Climate,” Policy Research Working Paper 6128. Sen, Binayak. (1996) ‘Rural Non-farm Sector in Bangladesh: Stagnating and Residual, or Dynamic and Potential?’ _The Bangladesh Development Studies,_ Volume XXIV, Dhaka.

Timmer, C.Peter. 2002. “Agriculture and Economic Growth.” In B. Gardner and G. Rausser, eds., _Handbook of Agricultural Economics, Vol. IIA_ . Amsterdam: North--‐Holland. 1487-1546. Wang, Shing-Yi. 2008. “Credit Constraints, Job Mobility and Entrepreneurship: Evidence from a Property Reform in China”, typescript, NYU.

World Bank, 2007, “Bangladesh: Strategy for sustained growth”, _Bangladesh Development Series Paper_ No. 18, The World Bank, Dhaka.

33
Policy Research Working Paper 6876

# **Abstract**

Housing matters to the livability of cities and to the productivity of their economies. The failure of cities to accommodate the housing needs of growing urban populations can be seen in the proliferation of poorly serviced, high-density informal settlements. Such settlements are not new in the history of rapidly growing cities, their persistence results as much from policies as from economics and demographic transition. Slums have attracted most of the attention on urban housing in developing countries, and the Millennium Development Goals have given prominence to their reduction. However, urban housing issues transcend conditions in slums and are linked closely to economic activity. City economies influence housing demand, and in turn the flexibility of housing supply affects economic activity and city growth. This paper provides an overview of the policy and institutional building blocks that determine housing sector performance, drawing on studies from both developed and developing countries. It focuses on property rights, including tenure choice, housing finance, and subsidies. It also reviews the factors that affect supply: the availability of serviced land, as well as land and housing regulations. While these elements have been standard tools of housing policy, they are still the subject of considerable debate over their sequencing, policy content, and priority. There has been a great deal of emphasis over the last two decades on demand-side measures to improve property rights systems, develop housing finance, and even to introduce new subsidy systems. All these developments are important, but as this paper argues, less progress has been made in understanding and addressing the factors that constrain supply, which often thwart improvements on the demand side.

This paper is a product of the Urban and Disaster Risk Management Department, Sustainable Development Network. 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://econ.worldbank. org. The author may be contacted at shammam48@gmail.com.

_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
We present a number of robustness checks. When we scale survey incomes to final household consumption from national accounts, we obtain a lower level of the global Gini index and a stronger decline over time.<sup>7</sup> We also present a simple robustness check for underreporting of top incomes in household surveys. We treat the discrepancy between national accounts consumption and household surveys, an issue which has received considerable attention in the literature, as a proxy for the extent of “missing” top incomes. We obtain detailed top quantiles of the distribution by allocating this gap to the top decile and fitting a Pareto distribution. The Gini index of this revised distribution is about 5 Gini points higher and does not decline substantially between 1988 and 2008. The difference in levels is primarily due to allocating the national accounts excess consumption to the top decile rather than to the Pareto imputation itself, that is the elongation of the upper tail of the distribution.

The regional composition of the global distribution has changed substantially over this twentyyear period. China has emerged from the bottom ranks of the global distribution, which has had a profound effect not only on the regional composition, but also on the overall shape of the global distribution. Both China’s average income growth and change in income inequality have been exceptionally strong. India has grown more slowly, but its inequality has also grown much less. As a result of the growth in China (and, to a lesser extent, India), Sub-Saharan Africa now makes up most the bottom part of the global distribution.

Not surprisingly, China (particularly the urban part) is among the country-deciles which have grown most between 1988 and 2008. In Latin America, some country-deciles have also performed very well. The new EU member states can be found both amongst the biggest gainers and losers, similarly to Sub-Saharan Africa after 1993.

In constructing our global income distribution we have tried to be as careful as possible given the data constraints and corrected some of the biases in earlier studies (see below). Nevertheless, sources of uncertainty over our estimates remain which are difficult (or impossible in some cases)

> 7 We want to stress that scaling survey incomes to national accounts is not our preferred estimation strategy. We simply replicate the commonly adopted approach in this literature which scales survey incomes to GDP from national accounts. Ours is the first paper to scale to national accounts consumption (rather than GDP), which should be preferred (Anand and Segal, 2008) as we argue in more detail below.

4