rafmacalaba/datause-encoder-data huggingface dataset:
Table B8: Profile of the poor across urban versus rural areas

||||**Poor b**|**y DOU**|||**Po**|**or by D**|**B**||
|---|---|---|---|---|---|---|---|---|---|---|
||All<br>(1)|Urban <br>(2)|center <br>(3)|Urban <br> cluster <br>(4)|Urban<br> Rural <br>(5)|Urban <br>(6)|Core <br>(7)|Suburb <br>(8)|Town <br>(9)|Other<br> Rural<br>(10)|
|**Demographic:**|||||||||||
|Household size|6.55|6.98|6.97|6.99|7.73|7.25|7.18|6.87|7.46|7.55|
|Age of household head|40.31|34.58|28.69|40.83|45.57|36.94|30.09|43.46|40.51|44.60|
|Household head is male|0.78|0.79|0.80|0.78|0.80|0.79|0.80|0.75|0.80|0.79|
|No education|0.37|0.41|0.50|0.32|0.44|0.42|0.49|0.34|0.40|0.44|
|Primary complete or incomplete|0.39|0.49|0.40|0.57|0.46|0.48|0.42|0.54|0.51|0.47|
|Secondary complete or incomplete|0.18|0.09|0.09|0.10|0.09|0.09|0.08|0.11|0.08|0.09|
|Tertiary complete or incomplete|0.06|0.01|0.02|0.01|0.00|0.01|0.01|0.01|0.01|0.00|
|**Employment (of household head):**|||||||||||
|Not in labor force|0.12|0.13|0.10|0.17|0.16|0.14|0.10|0.21|0.15|0.16|
|Unemployed|0.03|0.03|0.02|0.03|0.03|0.03|0.02|0.07|0.03|0.03|
|Employed in Agriculture|0.52|0.63|0.64|0.62|0.67|0.64|0.63|0.48|0.69|0.68|
|Employed in Industry|0.08|0.06|0.07|0.06|0.04|0.06|0.07|0.07|0.04|0.04|
|Employed in Services|0.26|0.15|0.18|0.12|0.10|0.14|0.17|0.17|0.09|0.10|
|**Access to basic services**|||||||||||
|Improved water|0.69|0.66|0.61|0.71|0.62|0.64|0.62|0.70|0.63|0.64|
|Improved sanitation facility|0.24|0.16|0.12|0.20|0.20|0.17|0.13|0.26|0.16|0.20|
|Access to electricity|0.48|0.30|0.31|0.27|0.21|0.28|0.31|0.31|0.26|0.20|

Source: International Urban Poverty Database. Note: GHSPOP 1km is used for the DOU and DB methods. Poverty is measured using the $2.15 poverty line.

66
that, in the former case, misalignment results from pegging to a misaligned anchor currency, while in the latter it results from asymmetries in the trade structure of the client and anchor countries.

The decomposition is presented in Figure 3.5, which shows the contribution of each of the three ingredients to the cumulative change in the misalignment of the peso between 1997 and 2001. As shown earlier, these were the years during which the overvaluation developed. Between those dates, peso overvaluation rose by 50 percent.22 The figure suggests that both the wrong choice of peg and inconsistent fundamentals were behind the mounting overvaluation of the peso in the final years of Convertibility. Indeed, the former accounts for 27 of the 50 percentage points of overvaluation accumulated between 1997 and 2001, while the latter contributed the remaining 23 percentage points.

**Figure 3.5. Sources of cumulative peso overvaluation, 1997-2001 (Percentages)**

<!-- Start of picture text --> 50%<br>40% - Inconsistent fundamentals<br>a3 Dollar overvaluation<br>30% _ * Trade structure<br>20%<br>10% ,<br>.10% B1999 2000 20 1<br>-10% -<br><!-- End of picture text -->

**Source: Alberola, L6pez and Serven (2003)**

The graph also shows that most of the misalignment due to inadequacy of the peg can be traced to the overvaluation of the U.S. dollar in the late 1990s. In contrast, trade structure asymmetries worked _against_ overvaluation prior to 1999, because the Brazilian real was itself appreciating in those years. The figure clearly shows that Brazil's abrupt devaluation in 1999 added significantly to the misalignment of the peso. In fact, Figure 3.2 above showed that the overvaluation of the peso increased by almost 20 percent in

> 22 As before, this figure and the ones that follow refer to logarithmic deviations.

24
# Table **1.0.**

Turkey: Unemployment, Urban Areas,<sup>1967-1985</sup> Turkey,, 1985 Labor **Eorce**<sup>Survey Data</sup>

||Urbana<br>|eNoK<br>|ember,<sup>a</sup><br>196|<sup>ge 15</sup><br>**___**_<br>_<br>|<sup>and abo</sup><br>**__**<br>___<br>___<br>_<br>___<br>1969|<sup>ve</sup><br> <br>____<br>___<br>|Urban<br>___<br>___<br>1982|placesa<br>___<br>_<br>|<sup>Oct.-N</sup><br>___<br>___<br>1983|<sup>ov.,</sup><br>___<br>|age 12<br>___<br>____<br>1984|and<sup>a</sup><br>___<br>|<sup>bove</sup><br>___<br>___<br>1985|__<br> <br>|Turkey,<br>as<br>urb an<sup>place</sup><br>1985|
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
||196?||||||<br>|<br>||||Pt|000|Ct|00<br>PCt|
||||000|**Ct**|000|Pct|000|pCt|000|pct|000|c||p|__<br>|
|_____________|000|pct||**p**|||||||||||616?<br>877|
|Employed|1,819|97.2|3,572|97.1<br>|3,369<br>|96.7<br>|5,328<br>|89.1<br>|6,075<br>6075|87.9<br>856|6,317<br>6317|88.1<br>856|6,912<br>6912|85.2<br>84.7|,<br>.<br>16,163<br>87.1|
|Employedb|1,819|96.6|3,572|96.5<br>|3,369<br>|95.1<br>|5,328<br>|87.2<br>|,<br>833|.<br>121|,<br>855|.<br>119|,<br>1203|<br>14.8|<br>2,260e<sup>12.1</sup>|
|Unemployed'|52|2.8|106|2.9|115<br>|3.3<br>|654<br>|10.9<br>|<br>|.<br>144|<br>1062|.<br>144|,<br>1250|<br>153|<br>2155e 12.7|
|UnemployedCd|64|3.4|128|3.5|172|4.9|781|12.8|1,021|.|,|.|,<br>|.<br>|,<br>|
|Labor force|1,871 1|00|3,678<br>|100<br>|3,484 1<br>|00<br>|5,982<br>|100<br>|6,908<br>7096|100<br>00|7,172<br>7379|100<br>00|8,115 1<br>8162 1|00<br>00|18,421 100<br>18,514 100|
|Labor forced|1,883 1|00|3,700|100|3,541|00|6,109|00|,||,||,|||

a 1967: 32<sup>cities</sup> with _50,000_ inhabitants and above

- 1968, 69: 32<sup>cities</sup> with 50,000<sup>inhabitants</sup> and above, 25 n n 15,000 " tt t 49 **n n** less than 15,000<sup>inhabitants,</sup>

- 198to 1 town it tt 2,000

**b 1982 to** 1985:<sup>**All**cities</sup> with 10,000<sup>inhabitants</sup> and above. c Including persons with<sup>a Job but not at work, negligible.</sup> d Seeking<sup>employment.</sup> e Including "inactive unemployed",<sup>1.0 percent</sup> of labor force. Including unemployed<sup>not seeking employment.</sup> Sources: Statistical Yearbook of<sup>Turkey, 1973, Tables 146, 150, 154.</sup> ibid., 1987, Table1s37/and 1985, Table<sup>151.</sup> ,146
_Table 2.6 Synthetic Indicators_ _<u>of Schooling Distribution and Income Profile, 1988, 1997–02</u>_

|_Year _|_1988_|_1992 _|_1996_|_2000_|_2002 _|
|---|---|---|---|---|---|
|_mt_|0.476|0.491|0.511|0.519|0.522|
|_it_|0.066|0.069|0.076|0.077|0.077|
|_st_|0.066|0.102|0.122|0.097|0.080|

_Source:_ Calculations based on the ENEU survey (third quarter).

# 3. METHODOLOGY

The dynamic decomposition analysis is a suitable tool for translating this stylized view into quantitative results, giving one a better understanding of the socioeconomic transformations responsible for changes in the earnings distribution. Besides permitting identification of the relevant individual variables, it also helps in understanding the nature of the contribution of each variable to the evolution of earnings inequality over time.

Ramos (1990), following Shorrocks (1980), shows that it is possible to break down the change in inequality between two points in time. This is done according to whether the change can be attributed to changes in the socioeconomic groups relative to incomes, to group sizes, or to internal inequalities, through use of the Theil T index. In generic terms, as shown before in a slightly different way, for a given partition of the population, the inequality indexes of this class can be written as: where α _g_ is the ratio between the average income of group _g_ and the average income of the whole population, β _g_ is the proportion of the population in group _g_ , and _Ig_ is the internal dispersion of incomes in group _g_ .

Of course, the αs are related to the indicator _st_ in the previous picture, and the βs refer to _mt_ and _it_ . In this context, the _population_ or _allocation effect_ corresponds to the variation induced in the inequality index _I_ by modifications in the allocation of the population 20
15 much faster. As noted by Blaug (1998), the two sub-fields took different routes with health economics inspired by Arrow's work on medical insurance, interest in the way that health institutions operated (especially the non-profit sector), in the economic aspects of wider political controversies about health care financing, in the measurement of the outputs of health care systems, in addressing issues of ̳equity‘ almost as much as those of ̳efficiency‘ and, especially of late, in the application of cost-effectiveness methods in the multi-disciplinary field of what has become known as ―Health Technology Assessment‖.<sup>17</sup> By the end of our period (2010) health economics had notched up over 33,000 publications, 12,000 more than education economics.

# **Articles and topics**

Table 1 lists the 300 most-cited publications in health economics for the full period, organized by topic and excluding publications, such as textbooks, that traversed the whole field.<sup>18</sup> The largest category ( ̳Determinants of health‘ – 16%) includes several classic articles such as Becker et al. (Becker et al., 1994), the fourth most cited article in health economics as a whole. It also includes a classic book by the Frank Ramsey Professor at Cambridge (Dasgupta, 1993), followed at some distance by Fogel (1994) again on a ̳big‘ theme. Other heavily cited classics include Smith (1999) on the health-wealth nexus, Mosley and Chen (1984) on childhood survival in poor countries, Pritchett and Summers (1996) again on the health-wealth nexus, Chaloupka and Warner (2000) on smoking and health, and Deaton (2003) on inequality and economic development. This category also includes several papers looking at behavioral determinants of health, the economics of ̳bad‘ habits in addition to the addictions, the role of education in shaping lifetime health, other determinants of lifetime health such as early parenting and childhood nutrition.

The second largest category is ̳Economic evaluation‘ (11%) led by three classics each having more than 1,000 citations: Torrance (1986) on standard gambles and health measurement, Weinstein (1991) on the key ideas of cost-effectiveness analysis, and Tengs‘ (2004) review of 500 life-saving interventions. Very close in size are ̳Public health‘ and ̳Health and the

> 17 According to Google Books Ngram Viewer, HTA took off spectacularly in 1980. 18 The percentages are defined in terms of the 260 articles among the top 300 that were assigned a topic. Not all of the top-300 were assigned a topic. Some, for example, were considered too general and some were not considered part of the health economics corpus despite their JEL code and inclusion in EconLit.
Green, David A. 1999. “Immigrant Occupational Attainment: Assimilation and Mobility over Time.” Journal of Labor Economics, 17(1): 49-77.

Jasso, Guillermina and Mark R. Rosenzweig. 1982. “Estimating the Emigration Rates of Legal Immigrants Using Administrative and Survey Data: The 1971 Cohort of Immigrants to the United States.” Demography, 19(3): 279-290.

Jasso, Guillermina and Mark R. Rosenzweig. 1986. “What’s in a Name? Country-ofOrigin Influences on the Earnings of Immigrants in the United States.” In: Research in Human Capital and Development vol. 4, Oded Stark (ed.), Greenwich.

Jasso, Guillermina and Mark R. Rosenzweig. 1988. “How Well Do U.S. Immigrants Do? Vintage Effects, Emigration Selectivity, and Occupational Mobility.” Research in Population Economics, 6:229-253.

Jasso, Guillermina Mark R. Rosenzweig and James P. Smith. 2000. “The Changing Skill of New Immigrants to the United States: Recent Trends and Determinants.” in G. Borjas (ed.), Issues in the Economics of Immigration. Chicago, IL: University of Chicago Press.

Heston, Alan, Robert Summers and Bettina Aten. 2006. Penn World Table Version 6.2, Center for International Comparisons of Production, Income and Prices at the University of Pennsylvania, September.

Hu, Wei-Yin. 2000. “Immigrant Earnings Assimilation: Estimates from Longitudinal Data.” The American Economic Review, 90(2): 368-372.

Kim, Seik. 2009a. “Economic Assimilation of Foreign-Born Workers in the United States: An Overlapping Rotation Panel Analysis.” Working Paper, University of Washington.

Kim, Seik. 2009b. “Sample Attrition in the Presence of Population Attrition.” Working Paper, University of Washington.

LaLonde, Robert J. and Robert H. Topel. 1990. “The Assimilation of Immigrants in the US Labor Markets.” 1990. NBER Working Paper No. 3573.

LaLonde, Robert J. and Robert H. Topel. 1997. “Economic Impact of International Migration and the Economic Performance of Migrants.” In M.R. Rosenzweig and O. Stark (ed.) “Handbook of Population and Family Economics.” Elsevier Science Long, James E. 1980. “The Effect of Americanization on Earnings: Some Evidence for Women.” Journal of Political Economy, 88(3): 620-629.

Lubotsky, Darren H. 2007. “Chutes or Ladders? A Longitudinal Analysis of Immigrant Earnings.” Journal of Political Economy, 115(5): 820-867.

Mattoo, Aaditya, Ileana C. Neagu and Çağlar Özden. 2008. “Brain Waste? Educated Immigrants in the US Labor Market.” Journal of Development Economics, 87(2): 255-269.

32
magnitude, statistically insignificant, and in some instances have a negative sign. The results are similar if we use alternative specifications for the consumer protection variables. We have also tested for correlations between policy variables and levels of financial access, controlling for income per capita and other country level characteristics, but also do not find consistent and statistically significant correlations.

These statistical results are not entirely surprising due to a number of challenges discussed above. While the data collected in the survey provides a useful comparison of information on financial consumer protection, it is not sufficient to conduct any serious empirical analysis or design policy indicators. Further refinement of the methodology requires micro-level studies, collection of more granular outcome data, and availability of time series data.

# **4 Conclusion**

This paper introduces and describes a new cross-country data set on financial consumer protection based on a survey of financial regulators in 142 countries. This is the first effort of its kind to collect information on the details of existing financial consumer protection legal and institutional frameworks using consistent methodology.

The analysis of the data provides a number of insights. First, most countries have some form of consumer protection legislation in place, though it often does not include provisions specific to the financial services industry. Second, enforcement powers and monitoring ability of supervisors are often limited. Third, unregulated financial institutions are rarely covered by existing financial consumer protection legislation. Fourth, regulations on financial consumer protections are often recent and many countries are actively reforming in this area. In sum, this is a new area of focus where much further analysis is needed to inform the policy making process and develop effective solutions.

To assist future research we also compile a comprehensive list of laws and regulations relevant for financial consumer protection reported by the countries in the survey in Annex I.

This analysis presents the first step in a systematic assessment of financial consumer protection across a large set of countries. Further refinements in methodology are necessary to better capture the effectiveness of the implementation of the existing financial consumer protection regulations. More importantly, a greater focus by regulators on monitoring compliance and collecting data on consumer complaints and on how they are resolved can help inform public policy. Regulatory impact assessments at the country level, including the impact on the users of financial services, as well as on the cost to financial institutions, are also an important 16
Table 6. Regression Results Accounting for River Flow Dependency, Dependent Variable: CP

||(1)|(2)|(3)|
|---|---|---|---|
|**D(LARGE)**||327.4<sup>**</sup>|−78.58<sup>*</sup>|
|||(67.16)|(37.37)<br>|
|**D(LARGE)×RFH**||48.50|157.0<sup>*</sup>|
|||(32.51)<br>|(55.79)|
|**D(LARGE)×RFL**||−97.29<sup>**</sup>|−30.12|
|||(29.81)<br>|(89.03)<br>|
|**UD(LARGE)**||262.8<sup>**</sup>|284.3<sup>**</sup>|
|||(87.93)<br>|(88.72)<br>|
|**UD(LARGE)×RFHU**||48.73<sup>**</sup>|50.66<sup>**</sup>|
|||(14.83)|(14.72)|
|**UD(LARGE)×RFLU**||6.400|−0.502|
|||(27.15)|(25.03)<br>|
|**D(SMALL)**|||6.284<sup>**</sup>|
||||(1.916)|
|**D(SMALL)×RFH**|||5.821|
||||(3.521)|
|**D(SMALL)×RFL**|||0.529|
||||(2.502)<br>|
|**D(SMALL)×D(LARGE)**|||18.46<sup>**</sup>|
||||(2.942)|
|**D(SMALL)×D(LARGE)×RFH**|||−10.86|
||||(5.599)|
|**D(SMALL)×D(LARGE)×RFL**|||−5.814|
||||(9.354)<br>|
|**SPI**|57.76|55.74|54.29<sup>*</sup>|
||(31.01)|(27.58)|(23.80)<br>|
|**EVAP**|−69.66|−73.31|−74.78<sup>*</sup>|
||(40.16)<br>|(37.73)|(34.81)|
|**RFL**|−47.62<sup>**</sup>|−10.11|−3.762|
||(13.51)<br>|(18.27)|(18.08)|
|**RFH**|18.34<sup>*</sup>|−4.976|−47.56|
||(7.200)|(16.53)|(22.88)|
|**RFLU**|−19.07|−11.13|−10.40|
||(17.06)|(14.33)|(13.23)|
|**RFHU**|−27.02|−33.56|−33.76|
||(25.86)|(24.79)|(25.19)|
|**CL**|CROP|CROP|CROP|
|**IV**|No|Yes|Yes|
|**Observations**|4,460|4,460|4,460|
|**Basins**|223|223|223|

_Note:_ Driscoll and Kraay (1998) standard errors corrected for spatial and second order autocorrelation are given in parentheses;<sup>**</sup> and<sup>*</sup> are 1 percent and 5 percent significance levels. CP (dependent variable) denotes cropland productivity. Regressions include river basin fixed effects, former homeland year interactions, interactions of homeland basin predicted dams with river basin gradient, perennial and 39
# **VIII. References**

## **Adoho, Franck, Shubha Chakravarty, Dala T. Korkoyah Jr., Mattias Lundberg, and Afia**

**Tasneem.** 2014. "The Impact of an Adolescent Girls Program: The EPAG Project in Liberia" World Bank Policy Research Working Paper 6832.

**Aedo, Cristián and Sergio Nuñez.** 2004. "The Impact of Training Policies in Latin America and the Caribbean: The Case of Programa Joven" , Inter-American Development Bank Working Paper #R-483.

**Alzúa, María L., Guillermo Cruces, and Carolina Lopez Erazo** . 2013. "Youth training programs beyond employment. Evidence from a randomized controlled trial." Working Paper.

**Anderson, M. L.** 2008. "Multiple Inference and Gender Differences in the Effects of Early Intervention: A Reevaluation of the Abecedarian, Perry Preschool, and Early Training Projects" _Journal of the American Statistical Association_ , 103(484).

**Angelucci, Manuela and Orazio P. Attanasio.** 2006. " _Estimating ATT Effects with Non-Experimental Data and Low Compliance_ ." , No. 2368. IZA Discussion Papers.

**Attanasio, Orasio, Adriana Kugler, and Costas Meghir.** 2008. "Training Disadvantaged Youth in Latin America: Evidence from a Randomized Trial". NBER Working Paper 13931.

**Bandiera, Oriana, Niklas Buehren, Robin Burgess, Markus Goldstein, Selim Gulesci, Imran Rasul, and Munshi Sulaiman** . 2012. “Empowering Adolescent Girls: Evidence from a Randomized Control Trial in Uganda”. World Bank Working Paper No. 75566.

**Betcherman, Gordon, Martin Godfrey, Olga Susana Puerto, Friederike Rother, and Antoneta Stavreska.** 2007. "Global Inventory of Interventions to Support Young Workers: Synthesis Report" _World Bank_ , Global Inventory of Interventions to Support Young Workers: Synthesis Report.

**Bryson, A., R. Dorsett, and S. Purdon.** 2002. "The use of Propensity Score Matching in the Evaluation of Labour Market Policies" , Working Paper No. 4, Department for Work and Pensions.

**Campbell, Donald T.** 1969. "Reforms as Experiments" _American Psychologist_ , 24: 407–429.

**Card, D., J. Kluve, and A. Weber.** 2009. "Active Labour Market Policy Evaluations: A Metaanalysis" , Austrian Center for Labour Economics and the Analysis of the Welfare State Working Paper, (0902).

**Card, David, Pablo Ibarrarán, Ferdinando Regalia, David Rosas, and Yuri Soares.** 2007. "The Labor Market Impacts of Youth Training in the Dominican Republic: Evidence from a Randomized Evaluation." , NBER Working paper 12883.

**Fella, G. and G. Gallipoli.** 2007. "Education and Crime Over the Life Cycle" , Rimini Center 15-07.

**Frese, Michael and Gisela Mohr.** 1987. "Prolonged Unemployment and Depression in Older Workers: A Longitudinal Study of Intervening Variables" _Social science & medicine_ , 25(2): 173-178.

23
- 17 -

Emergency Room Visits. Samet ct al.A analyzed the relationship between emerger,cy room visils (ERV) and air pollution levels in Steubenville, Ohio, an industrial town in the midwestern United States. Daily ERV (mean 94.3) at the primary hospital in the area were matched with daily levels of total suspended particulates (TSP), sulfur dioxide levels, and nitrogen dioxide levels for March, April, October, and November of 1974 through 1977. Daily ERV were regressed on maximum temperature and each of the pollutants in separate runs. The particulates and sulfur dioxide coefficients were statistically significant in separate regression, but these measures were highly correlated.

We have selected the estimated regression coefficient for TSP as the best estimate and have used plus or minus one standard deviation from the coefficient to generate high and low estimates. The results obtained by Szmet et al. indicate the following relationship: Change in daily ERV = .011 * change in daily TSP in Steubenvilie Since the appmximate population in Steubenville during this period was 31,000, and PM10 is _0.55_ of TSP, we annualize this equation and obtain an estimate ofCentra change in ERV per 100,000 = 23.54 * annmal change in PM10 The upper and lower coefficients are 34.25 and 12.83, respectivelyRestricted Activity Days. Restricted activity days (RAD) include days spent in bed, days missed fron work, and other days when activities are significantly restricted due to illness. Ostro examined the relationship between adult RAD in a two week period and fine particles (FP. diameter less than 2.5 microns) in the same two week period for 49 metropolitan areas in the United States. The RAD data were from the Health Interview Survey conducted annually by the National Center for Health Statistics. The FP data were estimated from visual range data available for airports in each area. Since fine particles